{
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  {
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1ceQlPBquuCkKH+E///nP/u+pp54qWKQIkdy/v/jULM/QDBMnbpVCLTELFeM2oIYx/nvNjdjq1avttj/SPioVtpW1FfekbelDLOx+pXEpXFcEIGv4vQ4FG9R58+b5/oB+gT/6CdrAsGHDCjwn0M6oZ8KE9YxWFRvrZgmLw2wQt3r1Gr8pjqVdqp1amFqP8QVyuGOLb8hRa9z1ek8a3nohqXiEgBDIHAJ8uFiMtnTpUk9M+ehBKNGK2EeDQkEozjnnnKLlgxSzixEfPPxcGhEhMGQkvoitaCQlbkKkQo0k6eFnk+lCpgZxdyUpjgBa+vj0P94MbDqVqWn8MNuH2QYT8eltYqdeIT8MfHCeHwqbl1ic4X3OH374YT/9TZsKF/HQptAoViq0I4hT2L54F+KVJi1apeVJWzi0thMnTvRadGsP5BF/3JA3G7jSXm699VY3YsSIArtq6hlvBhDc0M8sdVzNwKa7uNA+WFyLZw92cosP2is1o6k2H+ZNxLAjH/RT559/vseF+LCRZwHcRRddVNHMRrV5KBdehLccQnouBIRA2yLAQhKIKp1znEhYofmA4Xe1GAmyMJDjkCBzn/fwsZlEhOzdckdIGeQ2jN/SY1r1m9/8Zks+HuXynYbnENhw+h/CGPreheRAGMOte3Gkn+TTttjKc8jMlClTEosL4QgXP1pAyFU1gyHaEcQ6JM20sWpIs6WtY3EEMGVZtGiRd1EY9gdh/0B7uuaaa3x/wMYOto0u9cyW0aFQPxdccEFZE5nwne6ekyak04inxUc7ZV1CJSY09k61R/z/Mui3tOmnrrzyyoJosDen7ZczGyp4qU4XMmmoE5CKRggIgewhgK2bmTIUyz0fCTw2zJw5M1GLxgcmrsHhHruuJRGnYmmF90K7Oz5QfKji/oEJH7rVCt/X+QEE+OBCek3QuMcHIOZj1MLwsUbzHxc0svF65pqtY/G/nCSEoT2EwkYCEKUwL6Gf0jDP4XuQ2zAuCLy2Ew4R6v45GyaYP+4Q6zBm+93xu0SLWSwc7YW+A3JXi4TtgffDNhF/FsZPfuLtlLwwC4V22sTKYNfhMUyL+52dn7jAC8PFzyGxYBffKCMeLslsKB6u3tfS8NYbUcUnBIRAZhBAewoxZZqa6TYjv2gB0fxh01dKs0tBmSKE9ECUmD5Ha8dH7vTTTy/QprBFLBohI7MQWNvIAOLCFqMmLFAKZeTIkf5DsnLlymiKnjzWSqjDuNv5nKl+SIlhTr3Ep/8xcQjrhXovJtQR2ioc6RMfRBkzl3IbPtiU7m9/+1sfLfXOe6GmmXN2YTO3aMR92GGHdclG3B8q09aNmqLuknhObjDAxB83u4Nh4419v20QQj1RN6adZMBy9dVX+8EomyxYOPoTSB9xhBpV+ht23mPgheDpweKivsNnYRvgWdiOeUZcCG3C2i/xEY608TBDfpLyYn2H/TZo9+SVPomd1YwcE2fYBxIu7Kvivxd+D5j8/O53j7nnn38uwsSwI3ySvbsvUAP//cfHH3/8cQPjV9RCQAgIgUwggFbPNBuQiPBDFRaABWpsA2pTnmhP2O+ejwKLVkKtXfhevc5tYQzpxMlbvdJQPM4T229961sRFJCIBx980LuXwhQmCXuIq5lI8PLPf/5z7/eXxUxI0ntRQgknvI9tpplH0O7uv/9+v+Vrwiu6XScEKv3NEQ5pdB9QabGa0R+Vy4thV6pPLRdHvZ5Lw1svJBWPEBACmUYAghtq3WopTDM+dKTRjHRqKX8e3qGdJA2GSpW/VqJLnJCGZcuWea2dpcHUu2kK7Z6OjUGg0t9c2n6XachPpdg1puYKYxXhLcRDV0JACAgBISAEUoMAMwospMILgK24R7t7ySWX1KwtTk3hlBEh0EQEtGitiWArKSEgBISAEBAC1SDw+uuvF5BdFkjhNUTa3WpQVFgh4JwIr1qBEBACQkAICIGUIsCKexbMIZBd823aHROJlBZV2RICDUVAJg0NhVeRCwEh0G4IYOeLhs2mlyEh3bX9bTeM2qE8LELEg4fJF7842E5LHr/xjW9EjvYJeNJJJ5UMX+4hq/jxxsBqejw+4CIvDbaZ5fKt50IgbQjIS0PaakT5EQJCQAgIASEQILBz506/5TGuompZMBdEpVMhkFsERHhzW/UquBAQAkJACAgBISAE8oGAbHjzUc8qpRAQAkJACAgBISAEcouACG9uq14FFwJCQAgIASEgBIRAPhCoy6I17Ivee++9CDG2jdMijgiO1JywUw8+HU1Y5Ys/R632NUR0FAJCQAgIASEgBNoRgW4TXkjUL37xC793sgH0/e9/32+laNc6pgMByO55553n9u3b5zPUv39/19HREe3lnY5cKhdCQAgIASEgBISAEKgvAt0mvGRn79690b7yXHd2dtY3l4qtbghAdt98800fX+/evf3K33pF/q9//UsriOsFpuIRAkJACAgBISAE6oZAXQhv3XKjiDKHAHu879q1y73wwgtu27Zt3k+kzFkyV43KsBAQAkJACAiBtkZAi9baunobWzhMJNjP/eyzz3bf+ta33IMPPtjYBBW7EBACQkAICAEhIARqQEAa3hpA0ysHEIjv8S5chIAQEAJCQAgIASGQRgQaTnhZ1BbKpz/96S52noTZs2ePtyfFw0N828RyccSfy+vAJ4i/++67jj+8MVQi2OH+85//jOqjZ8+erm/fvl08OcQxt7g//PBDZ8/Ceqg0XotHRyGQhEBoK17snHsIO1I143kz07JdtppRrkrTSqon3RcCQkAIpAmBhhJeprznzZvntm/f7ss8aNAgvzf5yJEj/TX2nw8++JDr6Ljfh2FBFZ4DJk+eHNmC0rFfccUV7umnn45wYz/xyy+/3H/QsB3FK4Sl8ZWvfMXdeOONXUhz9HJOTiC5v/rVr/wfuA4dOtRNmjQpsfSQ1I0bN7rHHnvMrVq1yu/bznssbKPepkw5102efJbHlbip12eeecb97W9/i+J86aWX3H/913/567POOstdddVV7qCDDnLr168vGu9nP/tZN23aNDd16tTc11cEok4KEDBiR/tksMxgjCP37dye8WKx82L3wrDl4ir3PIyrXFrlnpdLq9zzavJSLq5yz8EfsXowguxv6p8QEAJCIGUINIzw0lk+8sgjnuhYmYcPH+6OP/54fwnZvemmm9ySJUsKSBMeBJ599lm3efNmd+utt3p/vkcddZS7/fbbLRq3evVqd+mll3rC+9Zbb7m1a9dGcUB44xri6MWcnIAthHTFihVRicEVQvr+++9H98ITNOwMLMA+Li+++KLHeM+etz3uaHFfe+21yNuDhYf8GgGGJCN8DJPi5TnpEf8tt9zSRYts8eqYPwToPxAjVeE57Q/p0aOHf/7RRx85/ri282qfh3HxrsVH+vFzrk3sPHzfwtszu7a47DrpeTyuStOy+OLvc80zex7GFw9rzzgSPv7c4rJwVj925H5cs25hdRQCQkAItBKBhi1a2717t9cuWuGYUmeBE2SUj9myZcsKyC5EOJx2h6ytXLnSvz569Gh3yCGHWFRemwupQ/AMYCSLMF/60ogoXF5P7r33Xvfwww93KT6k17CKPzz00EPdMccc429TD/H64L2lS5d6LTBaW8KG9cWL4M89/hikIGjjGOggxeLlPvlds2aND6N/QgAEIFBGoiC4RthC0mb3DLHwGfeqeR6GtXji9+y+pRcew7CWdvwYvm/n4bHYeZiGnRPOwoZpVPo8fCceVxiHhSuWlr0XHqkn6sw0wzZosTh1FAJCQAi0EoGGaXhXr17jNXdWuDlz5rhTTz3VX6IdZLrdyNeECRM8AWa3NjZGQOOHoCFmunvAgAF+St60j0y1b9261UHScIllwvXIkfkmvJgbgJthCzbgO27cOO9FwTA0zOzIQOQb3/iGGzZsmDvjjDO8thX/ygsXLoy09BBmtOnU449//GNHHc+dOydKC7MJNLV9+vRx2P5CjNH2TJw40RNgBi48I9477rgj0kCT10cffdSdfvrpPrzlScd8IQBBYqC8f/9+X3Cm/8sJPr979erlg9m5HblZ7LzYvVrDlour3PMw3XJhu/vcg/Tvf/WI67DDDvO/8dBWPyTHYXo6FwJCQAi0GoGGEF4IDGYHJqNGjfL2o2bjtW7dcxGpRSt4wQUXeFILscUkwQgvU/BvvPGGO+6447yW0Mga0/KYMqA5RMNrAuGKax3tWV6ODATAzQQ8FixY4LCb7tv38/5ZSIYtHMdvfvObBYQTwoHtLna9JpiaoMWhrj7/+b52OzoOHjy4y7bSY8eOdePHj4/CEO9//ufpXgtteaGeqVf58I1gytUJZPeuu+4qMF3KFQAZLCw2+PQtpsgw4huSXvoKu5/BIirLQkAItBECDSG8GzZsiEgXhHbmzFluyJAhHjbIzvPPP1cAIVq/nTt3+nucmxixPeGEExykialvCBJ/EF3MGtAWm5xyyil+Ct2u83hkIIAm1oRBgJkUDB9+kteKG8m0MHZkQAKmf/rTn7xtLRiH+BIODTIftGo+YoSl3hmw/OUvf/H22cQb5oO6NttMy4+O+UGA+mdmwga7+Sl5tkvKb9rWZfD7ZVbH+gb6CWx+GcyYsiPbpVXuhYAQyDICDSG8aBiNdA0cONCNGzc2wohOEG2eCaRn7ty5dukwVwiFqTdkxIgRjriMJKFphHxB8BCINZrkvHesocYbXLCltQ+QB6rEPzxeLF68uEDzWiJ4xY/w1sHiRMwWrF1U/LICtj0CECLIEqTXhN8zJkpZFspj/VU7lMfqIv4bNhMUzJjCQSv9jml7pek19HQUAkKgVQg0hPCGHeKOHTu8ref06dN8GdH0hR82bpbS6nR2HrDn+8IXvuA1lRYWsgtBs7ggw0cffXSrcExFuhAH+8BWmyFI6axZswq8NKC5wdtCaNJQbbxo7vEYEcZh8YbeNaqNV+HbFwHI4ZIlSx0zElkWBo/mKYW1CAzs6f+yLuE6C+zxi5UJTa8JpJdraXoNER2FgBBoBQINIbxhQSBg+Nn9+tcnJNpnYmeKv9e4YCM2ZMhgfxvNLRpc+4CgRUbLa8K0PZtW5FnACLJQi2AuYjbSvI9tHh9pPmjdIaYdHR0R2SVvxDlz5ky/mOjcc8+tmaDXUka9kz4EIEFo/+IycOAAhylTloX+ywRTLTPrsntZPFJfYbkog83CcY5W1zS+ds2ROsZji0QICAEh0CoEGtIDsZkAGljT6kGY2HyAhUt0iLi0MnIFCbr//vsjO7A4EKGmgFX+kGM0yPxhK2waTbwL5N3/LtixMC2Uv//97+Fl0XPsdh966KHoGV4dLr74Yr8wDc1vrUIbCBcvooVnkxAWvHUn3lrzo/eEgBCoPwKhGQOxmwmV3Q9NG/JuclZ/9BWjEBAClSLQED+8kE9W95tASu+77z4/9QWBPfLII+2RJ6ybNm3yZBXCGv8LO0g0uCzCMoFII5Dmk08+2W7n+ojnhFDLy+5pRi7feeedyATEQMLuDtduoe00nhLMfrLYdKW9Gz9CriG5JpybyQn30AxhmiIRAoZAMe2uPdMx/QjwmzezBggu2l76jFDLm/5SKIdCQAjkAYGGEF6AY6Fa6CKMbWghXxBYPC6EpIxd1JYvX+GJGTaf2OY+/vjj3mNAWAkQMfzJmph2F83hiSeeaLdzfcQ+NhwUYPPMgjE2drjnnnsijXgIEriGJiXUE++h+WXzD8M5fIdzSLERY66x18Y04oEHHvBeNyDTobDAkHZAvKzID8lwGE7n+UIAG08RpGzXeWjWkO2SKPdCQAi0KwINMWkArL59+7oxY8ZENreYIECG8NnI35lnnhk9g1yxgQHEFS0gWgM0jnfeeWeB/1bihcxBlkMShv2uzBkONFE0qKeddlpkMsJdBhRJHhIgGpgYgKEtCOR40UUX+QVrpkU/EHvhf3zu9u/fP/K8QJ2wUQX1w6Kj73znPB+vma/QBli4w0K47sWzca4AACAASURBVNgFF+ZCV0JACLQSARYW9+rVs+jiNcuXmTmg/bVze6ajEBACQqAZCDRMw0unxs5doSb36aefdi+//LInp9dcc43fAcwKCVmCaEGOOEKOwg0ULBxkOdRgcp9NDNSJHkAIDToLw1jgFwp4onGP37cwM2bMKNDIUwfYYKPBNT+bhA21smiGJ0+ebFFER+py//5Or80nL+H7SfFGL+skNwiwAArBV6skmwhgtsRvHdJr5gylSqKFa6XQ0TMhIAQaiUBdNLysQA7NF2yrT1ZZQ07NVy5a240bN/nV16xYXrZsmWMV/4MPPujDGJmCZPFenNgCBM/CRW+Qqay7L6p3BYMtW/yyfS8mBODKgj+2d8ZPr9UH2nQzO0Abzzs/+clPooEG+H/ve9/z3jBCG98wv+eff76/ZKtoTBpMevY8sN0rO7xhUsEAxwYwSfFqWtvQy9fRfLXmq9TtW1psee0bEJaSehbhDRHRuRAQAs1E4D8+/vjjj7ubIDaZ4eImzAvQuKLBMRJraXA/ND+wMGgKWPxAR8niNMKxwC1ctEYc2PeGfiBnz57tiZo0vIbwJ0fqBM0uRzagAHfOqS8TBhAhxtQDi9gQeydev2h2Q7E6ZFtjxAZAYZ0Qh204Qv0SR7m8hGnovP0QoP4RiBBtjlkGZniYFcJrCLb+WZZLL7002ioZN3833HBDlovj885vncGxmSnh0WXSpEn+GWZs9N/Wd9MPMKCmH7e+IOz7Mw+GCiAEhECmEKiLhpdOrFhHBpGKk6M4OhamXDjeg4zhzJ1pcYQP48SJE6PONB533q/5yMR9f3LPPj7F8KEe4nWRVL/2fiV1SBxxv6rl8mLx6ygEhEB6EcCUAaJrmwSV6l8gzOEAO72lUs6EgBBoNwTqQngbDQqaIDwHMEVvG0+QJgvfsq4FajR2il8IpBUBprflliyttaN8CQEhIATaC4FMEF6mw1n9bxtZUAXYDDMFWkqb0F5VpdIIASEgBLKFAHb54eZB2cq9cisEhEA7IdAwLw31BInpcOxJTSC7N9yw0NuS2T0dhYAQyBYC0u5mq76UWyEgBIRAlhHIBOFFi2ubVeCVAbKLj1fZgmW56SnvQkAICAEhIASEgBBoDgKZMGkAihEjRrgbb7zRu9c67rjjRHab0z6UihBoGAKtsOGNewahcMwgyTSqYdWsiIWAEBACqUAgM4SX3cBmzZqVCtCUCSEgBLKJANtrs611KBdccIE7++yzw1uZOsfzwWuvveZef/11n29mwdhxUTNgmapGZVYICIEGI5AZwttgHBS9EBACTUag2Ta8aHchu+HiV4rM+oCsEt6dO3e6X/ziF2758uWRz3N8a0+fPt1ddtllXVwMNrmKlZwQEAJCIDUIiPCmpiqUESGQLwSabdKwZ8+eiBSGSLMhCp5gMG3IkuCX/Ac/+EGBq0byz9beeLVhIx/MwLJWrizVgfIqBIRAdhDIxKK17MCpnAoBIZBWBNgJ0LbVDvO4ffv2aHfB8H6azzFjWLlyZQHZZSMe/kzuvfde94c//MEudRQCQkAI5BoBEd5cV78KLwTyg8COHTv9VtuUGDtXI4c7duwoSoTTjMzu3bsLyC6uGu+++27HFsYmaHp/+ctfFmwlbs90FAJCQAjkDQER3rzVuMorBFKCQDNteLHfff7556KSDx8+3A0cONBfQwwhw1kStld/9tlnoyyzxTp2yOeff74bNWpUdP+ll14qasYRBdCJEBACQiAnCIjw5qSiVUwhkDYEsOFtlmCji62uybBhw9ygQYPs0pNhSHFWJCS75Nm2WMdeFzJv8v777zvIsUQICAEhkHcEaia82JAhHO1Dwbnd5154bmHtXjysPS8VF++Uek4clT4nbB7E8A5xCc/D51YnpZ6DWTXPk8ISTyX1aeHyUFcqY+MQYIEX2k6TI488smD3RsjwRx99ZI9TfeR3s23btoI89unTx18ziLBzbqC9Dstd8JIuhIAQEAI5QqBqwmukiOlIzk2M2IT3w/P4c54Ve0588bDcI6xJ0nPLT7nnVgYLT7zhuaWT5aOVsVUYWx2AoZ1XkxerE8ph77VbHWW5fWUt7/ioRduJYO/av3//AsKbpal/fg/43TWhPJ/73Of8Jb53+/XrZ4/8EdIrEQJCQAjkHYGK5xSNeISAffjhh+FlpCEJNSXFzovdI6Ji94vdC8PW8px3evTo4fPOxwNBM2KEKssO28vVUzG8it3rLsbh++F5pWlZuHg9ERd1Rn1luZ4oR97FfnvNwAFCa8QPsstGNmh5TSDD+LTlftoF84y///3vFWczrg2u+EUFFAJCQAi0EQIVaXjjJAoyYoQkq1hYGezIx9c+wEZ8s1I28osWlT/KYGWyY7wcpnG1+3ZtR+4XO+ee3bdjGLa7z8O4LG9hGTi3OrLnOmYXgWba8G7evDkCio0Z+vbt66f+zVMDZHjLlq1RmDSfYJ4hEQJCQAgIgeoQSNTwokVYv359RauX9+/vrC7VFodGs4NbooMOOsh95jOf8bmBTCEcTauYdg0iBHPNmjUFq7VbDG1dk2e1+amnnurrKKyfeCJpr6d4fnXdXATQ3IYmAOysxu8eMwA8NZjmFy8O06dPa27mlJoQEAJCQAg0BYFEwvvggw+5uXPnRB+DpuSmSYmg1WEHonPOOSfSWBrxtSyYJjHNZIqdo2bNmhX5FrW8t8sRwnvPPfdE26OGdWQEuJlawnbBNW/liG84waIuSHBnZ6f77Gc/G8HBwjUGkWE7ix6m6OTwww+vKjdhGat6UYGFgBAQAm2EQFHCS6ePtsM0H21UXl8UyoVbH3xXxoWPnWl500x6MWPYv39/tBAnXo52uGZXLNpi3FbcCImRXsqa5oFJO9RFI8pgv69GxB3GGW44wf3ly5e7hx56yAdh0wkTdlx788033ZAhQ+xWKo+YZFRDYkOvDakskDIlBISAEGgCAkUJbzxdpv9D347x51m53rhxY4FPSghjMckK6Q3zjtb6zDPP9Atx9u7dG7kmyto5C2wefvjhaLCFFs4Iblheu2ekV4Q3RCcb52jnG016GRi++mqhbS6ktpjs27fP77iWdsJL3o855pjIlIkFd/xOEMq7a9euqHj0C1/84uDoWidCQAgIgbwi0IXw0mEaiTBQ2L1n5syZdpnZ40033VRAeNEehtKzZ89ImxiS3jBMms7D/GOLeM0116ReO1UOv3Xr1rlnnnkmIrxod8NyxuvI4qPdivQaGjoaApDBDRs22KU/4sbLhOc2kwURzsKOa7RzNs5YsWKFLwb5Z8HdyJEjff8V98owZIgIr9W3jkJACOQXgS6EN0njwg4+7SbvvPOOX7gWlssIlWkQw2dpOYfcUU9pzmO9sILsmvbK4rQ64toGJpyL8BpCOhoCDJjCjRewC7/llltcr169fJAnnnjCLVy4MCK9aIOzMHg6+eSTHdpbI+t//OOTbsqUb3vtLjNZJkOHDnWDB4vwGh46CgEhkF8EuhBeoAg1au0KDa59rJx4awjLDaHiWVrJVNKgpB3rKsmkgTqKSxaISjzPeb5uRjtmwRpaXBNMs/izwWJ8MIV2FJKc9gH+iSee6EaPHu0ee+wxXzTMgLDrxT9vuJXw5MmTHTa/EiEgBIRA3hHoQnjj5gztDFD8Y2daHyuzkV6u00imjLBbftvx2Nl5wM6aurGBiZXTFrPZwEQeGwyZbBybYcP7yiuvRFpQUGE9gpFdrtF+QghNU4p2FIKcdsKLp4YpU851a9eu9Xkn/7fffntBxaPNnjRpkmY+ClDRhRAQAnlFoGDjCUid+aDNAyCQKUgvxBHyFBJgI1PgkKdBQJrqHeIR+ni2OqK+ii04bIbGME34KC/lEcAbSyjh7mrch/wy7W+C14b33nvPLlN9nDz5LLdgwQJv2hDPKMR+0aJFmbfpj5dL10JACAiBWhHoouGtNaKsvmcaRNP6mNY0PmUOmUqDjSiDEiRPJNzqiHLHtbxZbXfK94EtohuJA79lCO6ECRN8MmhFjz766IIkGeCjBQ0lHPiG99N2jhaaxcR9+37eYcNrm2tgsjF16lS/iC1teVZ+hIAQEAKtQqAL4c0LkTLtYc+eBxav8HFMIlN50nq3qiEmpQv5oI4gvZg1mOadAYppeW2wkhSH7ucTAUwmrrrqqshWn3YS/41zb8aMGX4TGkMp7eYMlk+O5JXd4dD24o6MgfoXvvCFVAzOw3zqXAgIASHQagQiwovmEC0m5M60nK3OXKPTTyJTpBtqeBkExD+Ujc6b4v8EAcwaDpDeT3zy0kYPO+ywLvWSRlvrT0qisxCBRtvwMiMDISxHYCG9WR80UcYs+A8O61/nQkAICIFmIlBgw9vMhNOSltmIZmUaMy24NSsftpjI0gsHY2h4TeNrz3UUAkJACAgBISAEhEAcgS6E10wacG8jEQJpRCBOckMSnMb8Kk/FEdAiw+K46K4QEAJCQAjUH4EuhFf2qp+AHCdWnzzRWTMRkPa9mWgrLSEgBISAEBAC7YdAF8JrGl6cmOddZLeb9xag8jcSAflNbiS6ilsICAEhIARCBLoQ3vBh3s+l4U1PCzBb6/TkSDkRAkJACAgBISAEsoJAIuGVDa98vmalESuf2URANrzZrDflWggIASGQRQQit2StyDwupNCihouOzFdmGjZ5aAUmaU3T6gobb6aiVT9prSnlSwgIASEgBISAEIgjkEh4m2HDy85A8+bNcy+99FKUr/79+7uOjg43YMCA6F4rTnB5JRte5yC6L7/8slu5cqXbsGGDY7cqdq8655xz3AknnNCKqlGabYJAo/3wtglMKoYQEAJCQAjUAYFEwluHuMtG8dZbb7m1a9e60Nfqm2++6bZu3dpywls28zkJ8N//fZ9bvPgm9+KLLxaU+KGHHnKLFy9248ePL7ivCyEgBISAEBACQkAIpA2Bltrw7tixs4DsGjivvPKKnbbsGO601rJMtDjhdevWuWuvXdCF7JItCPD8+fPdCy+80OJcKvmsIiAb3qzWnPItBISAEMgeAomEt9FFwW73+eefK5pMXJtYNFCDb2LSkGf54IMP3B133OHQuJtMmDDBjRo1yi496cXUIbTBjh7qRAiUQSBPbskOOeSQMmhk47Fs97NRT8qlEBACXRFINGlotA0v/n43btzoc8THYOjQod6WF/MG7kOiWrm/fd41vG+88YZ75plnohYD2V2+fLnbtWuXmzRpUkSEMW247LLLWlpXUSZ10jYI7N271+3cuTPT5Qk93dCvZb08VEbeFQGZbpDKvBDIOQKJhLfRuECcbLHaoYce6oYPHx5d86FAszhkyJBGZ6Nk/HiQaCXpLpm5Bj/EtjrU7o4bN84vWAOPMWPGuBUrVvgc7Nixw9tcs5hNIgTqgQDk8Ic//KFr9KC7HnktFQdrFEwYLK5atcouM320fjvThVDmhYAQyB0CiYQ31E40ApXQ9rN3797u+OOPj5KBRL3++ustJbxoMvJK4vDM8Oyzz0b1wQkaeATPFXhpMIGcbNq0yY0cOdJu6SgEKkKglA1vGsyaKipEhYEYPIYDyApfUzAhIASEgBCoEwKJNryN1q5s3rw5WrA2aNAgN3r0aIemF4FEbdu2rU5FrC0aTBryvNPau+++GwGHyYnVDTZ8cXtEtPUSIVAtAnmy4a0WG4UXAkJACAiB+iKQqOGtbzKFsWGfGxJaNKlHHXWUQ9NrgoanlXa8edbwonl7//33rSoisms3evXqZaf+iL2lRAjUCwEGVDfeeKMfBNcrzlbEg9s+M/2ZPXu2mzlzZiuyUfc0zzvvvKKeW+qekCIUAkJACNQRgZYQ3j179jg2nTAZNmyYYwcv7HhtKpNFU3gKyKsNrWHTiiP1U41JSzVhW1EepZk9BI499tjMb2wSzpIxoG+XjVrCcmWvZSnHQkAI5BWBRJOGRpIYVivbgg60OdiEMr0ZdqTbt29vqUlBnk0atBI7r91Bc8tdyoa3uTlpfGqYabWDYN8vEQJCQAhkEYFEwhuSz3oXbMuWrQULOPDGgG1ofOFaK934QPryurVw3l2y1bu9K77iCMiGtzguuisEhIAQEAL1RyCR8NY/qQMxYpcbbjjBYqh33nnH79jV2dkZJWur/6MbLTjJ66K1vn37Fmjby0Efem0oF1bPhYAQEAJCQAgIASHQbASabsPLhhPY55rgqgcfr8XE7HmLPWv0PbScedXwonkzrwzgHC5g4zocmHAd99rQ6LpR/EJACAgBISAEhIAQqAaBRA1vo2x4IU+VOi6HGKMRboXk2Y4V85JTTjklgh1tu5Fe6iMcsEB2zUdv9IJOhEAFCOTJhrcCOBRECAgBISAEGohAIuFtlA0vWlsjT5TriCOOcKNGjYr+Qm0hC9fwGNAKybsdK54zQrGNKPCcERJeNMFHH310GFTnQqAiBGTDWxFMCiQEhIAQEAJ1QCCR8NYh7qJRoN0NVyzPmTPHdXR0+L977rmnwPfmvn37Wrr/fF5teKk4XCgxGDF56KGH3Lp169xTTz3l2HbYhG2Gw3B2X0chIASEgBAQAkJACKQFgUTC2yiTBnZYM0Gbe/LJJ7sBAwb4P7w1hFPp2PeybW0rJM9eGsAbEjt9+vQIejTz48ePdxdeeGE0YKH+vvGNb8hXcoSSToSAEBACQkAICIE0IpBIeBuRWdyMhRtODBw40PXv378gqX79+hUsgmrVtrV59sNLhbDhx9SpU72piVUQmvlQOz937lz3ta99zR7rKASqQkA2vFXBpcBCQAgIASHQDQQSCW8jbHjDDSfI86BBgxwENxR2WAqFLYixG2225F3DC95o3O+88043YcKEgkEI2t8FCxa4yy67zB188MHNrhql1yYIyIa3TSpSxRACQkAIZACBprol69Wrl5s4cWIEy5e+NKILYTrxxBMdmsO9e/f6cGzJiWcAEasItqaeYMu7fPlyt379esfgo2fPXm7IkMF+G2ht+9zUqlBiQkAICAEhIASEQI0IJBLeRtjwHnfcce6WW26JslpMw3P44Ye7q6++OgrDSSuIlZk0tCLtgsKn4II6wX5XIgTqiYBMGuqJpuISAkJACAiBUggkEt5SL9X6DP+u/JWTNJBMTBogehIhIATaBwFmi9j8JhQ2mKmkXwrf0bkQEAJCQAhkC4FEwtsIG94sQSMNb5ZqS3nNIgLM8DRby9vR8Wv3xz8+WQDXeeedl9kZDNZFvPfeewXlCS9YI6GBe4iIzoWAEMgrAomEN6+AhOXO69bCIQY6FwLtgsC//vUvT3ZXrFhRUCQG91k02aE8P/jBD9zGjRsLyhNezJ49282aNSu8pXMhIASEQC4RSCS8jbDhzRLC8tKQpdpSXrOIQLO1u7t37y5wi2iY2RbmaTClsjxVcmRjHNw84iM7STo7O5Me6b4QEAJCIFcIJLolyxUKRQqb962Fi0CiW0Ig0whs3brVvfXWW13KwBbmbHKTNWGL9rwrJrJWZ8qvEBACrUMgUcObdxteaXhb1yiVcj4QaLYN744dOyNie/zxx0ea0R07dngijN/pLAm2u2y/bjJt2jR35JFH2qU/spOlRAgIASEgBJxLJLwCRwgIASHQLgjgneHVV7dGxRk+fLgni2h22T0QMpw1QVuNlhdhm+8ZM2a4sWPHZq0Yyq8QEAJCoCkIJJo05H2qTCYNTWl/SiTHCDTThpfdGjds2BChjSZ06NCh0TVkmEVgWZK3394TZffQQw/tsk179FAnQkAICAEh4BIJb96xwaRBIgSEQHsgwAKvl156KSrMsGHDHLs4mkCGCZMl2bPnba+dtjwzSG/FNuyWvo5CQAgIgTQjkEh4827DKw1vmput8tYOCBTbabFR5cKTgU3/H3HEEV4bGhJeyDBmD1kRtNGYYphgmvHlL3/ZTZkyxe9muWXLFnukoxAQAkJACDgnDW+pVpA1jU+psuiZEMgzAhBaI4j9+/d3AwYMKFjgBRnetWtXZiDCHGTbtm0F+YX0PvbYY+7KK6/09rzr1q0reK4LISAEhECeEUjU8ObdhldeGvL8s1DZm4FAM214N2/eHBUJe1f++vTp4xd78QAyvHHjpihM2k/YHhk7ZLxN8MeitVCeffZZt3DhQsdObBIhIASEgBCo0EsDH4vHH38883jFNSKlCoRJAxrerDijx7XSokWLXNZNURho2dRzqfrRs+wj0Cy3ZO+++27BhhOYMhx88MHuc5/7nBs4cGCk+Q29OKQdXXaBvOyyy9zFF1/sGJy//vrr7tFHH3X33ntvVJ61a9e61avXuOnTp6W9OMqfEBACQqDhCCS6JQuJ08MPP+z4y5PwEcnSHvRoqOJbpuapvlRWIZCEQHzDCTS7aD7ZhSzs51i4hh1vFga5n/rUpwr6J3wIY8OL3H777f5In/D888+5KVO+nYkyJdWf7gsBISAE6oFAIuENIzfbt/CezoWAEBACWUBgy5at0YYT5Pehhx5yq1at8lkPPTfYjmtZ24DC6gCt9dlnn12g5WXbZMwfskDirRw6CgEhIAQagUAi4Q1teG1VcyMy0Mw4cdRe6RaiWTNpoI4mTpxY4GqpmdjWKy0+0OG0bL3iVTzpQ6AZNrx4M4ibKuCxoZiwaxl9RFYJL2ViQZ5ECAgBISAEuiKQSHjDqb45c+a4yy+/vOvbGbtz/fXX+4UclWQ7ayYNvXv3dnPnzs30x5p6YWU5toiaVaiklWY7TDNseLHDDzecKIUYg+Es7rgWlgnCHgpmWT169Ahv6VwICAEhkEsEEglvHA1sxvIkWdPw5qluVFYhUCkCLIDEVMFk1KhR3k9tr169/K0nnnjCu/Gy57bjWtr7OzTXaMhDUwU2nbjnnnsKBotssBGGsXLqKASEgBDIGwIVE968AUN5WQktEQJCILsIsDgNDyYmw4cPd6eeeqozQsvCNVx62YwCnlzQCmMPm2Z5+eWX3bJly7xLsmOPPdbt3bvX/fa3vy1YXIy7sjPOOCPNxVDehIAQEAJNQyCR8IY2vE3LTYoSkh/eFFWGstKWCDTDhnfTpk0RmQVEXJIZ2eWaDSjwyWuEd+PGjd5TQ9oJLy7H8MYAWSf/aLKtDJSL+7Nnz3bHHXdcW7YdFUoICAEhUC0CiRtPhDa81UbaDuG1tXA71KLKkGYEmrG1MBswhDJ06NDw0pPF8B7a4LTvuIY5gy28g+RiexySXRawLliwwE2dOrWA3BcUXBdCQAgIgZwhkKjhzRkORYubpY0nihZAN4VAjhHAphVhah8ZNGiQO/roo/25/WNB1ymnnBLZ+TLQf+edd+xxKo9oqPHIwiwcGmm8SyB4aMBkA9dk2CrLdjeV1adMCQEh0CIERHgTgM+al4aEYui2EMgtApgl3HXXXQXlj5NArq+77jp39dVXR+GaoXmOEqvxZPz48e7000/39sa2M2Hfvn0deQ9NNmqMXq8JASEgBNoOgUTCm3cbXnlpaLu2rgKlDIFm2PDGCW4xCCCIWSSJ5BlSn3Z742KY654QEAJCoNkIyIY3AXEtWksARreFQJ0QyIImtU5FVTRCQAgIASHQYgQSCW+L86XkhYAQEAJCQAgIASEgBIRAXRBIJLwyaejp7ePqgrIiEQJCQAgIASEgBISAEGgZAomEt2U5SknCMmlISUUoG22LQDNseNsWPBVMCAgBISAEqkIgkfDKD680vFW1JAUWAlUiIBveKgFTcCEgBISAEKgZgUTCW3OMbfSithZuo8pUUYSAEBACQkAICIHcItB0t2TsErR792733nvveQfv//jHPyLwcZx+2GGH+e0+o5stOpFJQ4uAV7K5QUAmDbmpahVUCAgBIdByBBIJb6Ny9vLLL7tZs2b5XYLYIcicppPewIEDHaYUp512mt8Wc8iQIY3KRtl4tbXwAYiWL1/hXn11ayJeY8eOdfxJhIAQEAJCQAgIASGQVgQSCW+jbHjZtvOll14q2PvdwLH94J999lm3atUqd+edd7oTTjjBHjf1KA2vc2jjOzrud4899lgi9occcogIbyI6elAKAWx4peUthZCeCQEhIASEQL0QaLoNb9wuFsLEvu/8cW4C6V28eLELTR7smY7NQQDte6iBb06qSkUICAEhIASEgBAQAvVFIFHD2yw/vJgx3HPPPe7www93v/vdY27u3DmR9veZZ55xGzdudCNHjqxvqSuITSYNzr377rve9MTgOuKII1zv3r3t0h979epVcK0LIVApAtLuVoqUwgkBISAEhEB3EUgkvN2NuNL3MZ2A7PI3efJZBVPoaBffeuutSqOqaziZNDi3d+9eh501gvZ9zpw57owzzijAuV+/fgXXuhAC9UCAtseAK8sSVxpkvTxZrotK844ZF+tMnnjiCbd582Z35JFH+tlH1il85jOfcTt37nQdHR1eKTN16lQXrjNZs2aN448+kWcHH3ywb8MrV650u3bt8vdQpPC+xX3OOed4sz1mMnn30Ucf9Vk9/vjjozgqzbvCCQEhUBqBRMLbKBveUtmhgzjqqKNKBWnaM2l4nR9smEnDoYce6sluq2yqm1bxSqhpCCTZ8GLLf+GFFzraXJbFfjuUYcmSJW758uVZLk6U9zfffDM6b6cTSGdHx6/dtdcucJSRQT5tEfL5wAMPeHIL4V26dKl/PmzYsC6Ed+HChZ4gQ2T5niGPPPKIW7t2rT/fsGFDwZqIhx56yN11111u06ZN7vvf/340u0naL774orvxxhujeNoJa5VFCLQCgUTC24rMfPDBB+6NN96IkuaDF46gowdNOvnwww/9qL5JyaUumc7O/VEHTOZwGScRAs1AAKJhi1ibkV6j02i38jQar1bED8mF7DJQmT17tl+Mu2fPHtfZ2ekw56pF0Aoj1D8DHlxv3nzzzf6aQRCk9qKLLvIzaWiFIdcQZBYK33vvve7ss8/WouBagNc7QqAIAomENz4dV+Tdut6C7PIDt5EwkU+cOevjRgAAIABJREFUOLFlhBeTBsws8ix79rwdFZ+PAFNxTPGh5T3mmGPcpz71qei5ToRAtQiUsuGtlWBUm4dGhuc3Y6QdjV3WNdaGVVguu9cOx9WrV3vNLQuoQ80qml8jrt0t56JFizyBxXQCQSMM6Z02bVqU5rhx49z27dv9fcwcxowZo762u8DrfSHgnEskvM1CBxdl8+bN87ZOkF37QEyYMMHNnTu3bh1NteXBpCHPGl46ZKsLsOP8yiuv9DDyQZg8ebKbOXOmptuqbVgKXxYByCHTu6NHj/baNV5gcSSatiwd8TKzYsUKX160d/xeKENWy2N5RyMZ9g2+QG3wDztbhIFJ6E2oXmR36NCh7tRTT/VpoCzAJMLMJs4777yoL2VWc9CgQZ7wbtu2zbvuk3KhDRqYitByBBIJb7NseOk47aNgaEB2GWG30pwh7xpeyD6dbTHBZZz5Ur766qtbNigpljfdyw4CSTa8lODYY49tmQ/ueiEY9qGsTWgX+/ewXPXCKg3xfPGLg3026NuefPJJd/rpp9dVs0ob6NGjR1TUPn36ROdx7b+tZdFCxwginQiBbiPQdD+8leQYTS8annXr1lUSXGEagAAdM1qHn//8597mjCm3cJqZgQo2abiNkwgBIVAagXbRiNpUfOnSNuYp34Nrr73WLyDDBK7eMm7cWL/gDFtedgNlMVkj0rF8y8e8IaGjEGgOAoka3mbZ8EKipk+f7kvLilXsmfg4YLSPrRg+eluh6c27SQPTeOPHj49aIR3/+vXr3TXXXOPQ8CJ8GKgnpuk05RZBpZMKEShlw1thFAqWEwTQdF5xxRW+78EMABMRlCIDBgyoGwLEdcstt0TpED/fo3qnU7cMKyIhIASqQiBRw9usaStWrV522WXuhhtucPfdd58fYVsJIFYsZGuFVkF+eK0WDhxxsYMvSjS9oeBmB/MHiRCoFgFMGiRCoFIEbNofhcjtt9/ubaJZ1FVPYZMjtrTHrM7SwbxO2th6oqy4hEBrEEgkvK3IDjZuECpG8CYQKjS9zRbT8DY73bSnxwrisH6oG30M0l5ryp8QyDYCeMyBeEJETZhdmjFjhtfK1tPWle8Q5loLFizwSaF0sVktS1tHISAEsodAqggv8PXt27cARdyz1LMzK4i8zEW4UrdM0Nw8jpNbtC71WsWcGxBVUCEgBKpGACK6bNkyT0Rt0I1ZFa698PTzwgsvVB1n0gsQbGYe8UiDprfemuSkdHVfCAiBxiGQSHibZcMbLxqOvkPBtKIVu55h0iDpikDoOo6n8ZXHXd/QHSFQHAHZ8BbHRXeTEcDOFs8wd999t9+kgZAQUjz9sNCMHdHig/Lk2Eo/wYxr+PDhPhBbXSN4Vujdu7c/D73YYHZnYfxD/RMCQiB1CCQS3mbZ8IaIsG0ju8zQgZmwwUFc62vPGnlsBcluZHmqjZtFaqyK5khnzkfk8ccf97ZzFhdaFttj3u7pKAQqRUA2vJUipXAhAswosQMZ5DY0gcPsgC2pf/SjHzm+JdWK9XP2Hn2f7fzJDmhIv379nH0b2aiC2UfeY0Hv008/ba/qKASEQAoRaPmqkbfeesutXLnSO2R/8MEHC2ylIFS4xmrVlHmeN57405/+5DUmLCpk0PHaa69FvnetHZ955pnuq1/9ql3qKASEgBBoGgJ477n11lv9Bg5Lly71XmNQlmDiwNoPbHCr8SDz3/99n3v++ef8IB5NLoQaO2HILmsXELS+bLoDuWa2C1MKdp9ctWqV3x64aYVXQkJACFSNQMsJLzZY3/3ud7tkHLLLTmtoEFshed94gg6duuGv2IINbNvmz5+f++2XW9E2laYQEAIHEMDW9vLLL3cnn3xygctEiCobSOBSDBdmENVSgpb21Ve3+hksPECYQHbZMS90jTllyhSHdpc0bNMk+sM5c+Z4so05YCWea1DksAYinNG0dHUUAkKg/ggkEt5G2fCW6wggumzBOHPmLDd58lkt0+6al4ZWaZfrX9XVx4iPZLwwWIdM3dBBT5w40bsEapedo6pHRm/UAwHZ8NYDRcWBD3AUIx0dHd6TA14V6LMYrJsvXZQnIWmNo0YcF198sV+khm0u2yijuaWPY4YrFOyIWTy3evUaT5LZoY1NK1jkzDuIuVBjAx+2ZJ8y5Vw3ZMhgF5rxDB482HuY6Ozc7zgPBZL+pS+NcAMHDih4JwyjcyEgBKpDIJHwmp1SddGVD80Pm84HA/+QVJMeC6Agu4yq6+lQvHyuuobIu4b3qquu8h+RV155xdke83TsdNos5MjzQKBra9GdWhAotbVwLfHpnXwjYBtHQBQXL74p2sQIjS07QmLiUGq7YN6v9LtDuOnTC32Sgz62xaHQTybNUpZKD3/A/EmEgBCoHwKJhLd+SRTGxI+cTSYQFgUgjIJFoDwUqflnG00kddapyagyIgSEgBD4NwJ8RyCiw4ef5E0RzOQAsyy8OLCrJ+7GMIWQCAEhkC8EEr00NAMGSBV/aSS7ZtLQDByUhhAQAs1DAHvN8K95KSulZiGAKQIL2m6++WaHaRaCiYP57MUDjUQICIF8IZCo4Q3NDfIFyYHS5t2kIY91rjI3F4FW2PCybaxp/SgttpYXXHBBl6no5iJReWq42wp9hKOpLKWtxG0WJkl4w8HjCm61SoWvPCfpD0k5L730Um8mB9G1xbfUPyYO8+df5aZM+XYqFS7pR1c5FALZQyCR8DbKhjcrEJmGN43a56xgqHwKgVIINNuGF60ufr6N+FjeWDsQt720Z2k5btmyxef9V7/6lduxY0eUrSVLlha1JaWsTz75pMNdl20Ww6LT0aNHe28C48ePj+Jo5xP6b8p69NFHuyVLljhb0Pbiiy+6uXPneDdkLGyr1Ha3nbFS2YRAuyOQSHjbveCVlE9bC1eCksIIgWwgsHv3bu91JJ5bNhdgPUE511Xx95pxjYb2d797zC1bdmcXol4q/WeeecbbrDKNb4LnAnPXhaY7L6SX8uOh4cYbb/QLojmCC3iwoI36x5PCmDFjHN4aJEJACLQnAok2vDJp0NbC7dnkVaq0INBsk4atW7f6qf14+bdv3x55Iok/a+U1WtrbbrvN/d//O70qsgtJvuaaazyps/yj3TUxW1bC5UkY0LBw7f7773cTJkyIis4gYMaMGe6nP/2p3zkteqATISAE2gqBRMLbVqWsoTB531q4Bsj0ihBINQK42DONJ64PjQRiIoCNa9qFxVeW51J5RSPMpgsmbL8bJ3k8f+qppyxIro64+8KPLm7KDE8bBLBzGuYjEiEgBNoPgUTCm3cb3nBhSPtVu0okBFqPQOiEv9G5+cc//uH9slo6+JIeOHCgv2RqGzKcNmF6nR28ILqzZ8/2pJXtvEsJphl//OOTBZvFXHLJJd58gcV5JpR5zZo1dpm7Iza7V199tbv77ru9mQMAgAkL2rDnZlth2oxECAiB9kEgkfC2TxFVEiEgBPKOAEQQW02TYcOGuUGDBtmlJ8NpJDinnnqqJ7q33HKL47ycsDMiHghMIPW2ixdeGsxFF88JZ77QLXyejixog9zed999Di24aXtZ0HbhhRe6H/3oRzJxyFODUFnbHoFEwpt3G16ZNLR921cBW4xAM2142dI8nOZnC1i8M5hAhj/66CO7TM0R11pMwVfqLea9994r8OIAqbd3+/Tp43r37h2VjT4+HARED3J2gs/eO+64w5s42IAAbS+uzNioQj57c9YgVNy2RSCR8LZtiSssmEwaKgRKwYRABhBAa4f2E4HUoO0MCS9k2J5noDiJWYzbIkOY2ckS4Tw0Vdu3b5/r7OxMjCtPD1jQdvnllzu8V2BGYsKCtosuusjfz7M23PDQUQhkGYFEwht2jFkuYK15l4a3VuT0nhCoDIFm2vBCaNHaIZBdbDjR8ppAdtnUIevy9tt7onJSlng/zkYbJpS5HUi+lae7R2ymcdXW0dHhbaZDEwd89fKnBW3dRVnvC4HWIZBIeFuXpfSkzDRoliSNNohZwk95bU8EcO+1efPmqHCQvr59+zqm+I3UQIa3bNkahcnqyf791WlspeHtWtMMhrCZZlMPvHkgtA989uK+7PHHH/dbU3d9U3eEgBBIMwKJG0+ENrx8LFi1mnXZtm1bxUXI2tbCTE/iaiecpq24sCkKiE2htE4pqpAGZqVZNrxsOPHaa69FJeE3gl3r5z73Oe+pwTS/zz//XNFdy6IXM3AiAlufSqJ9TJ8+zQ0ffpJbvHixe/jhhz3pZZe+c88919v7nn/++bnZprk+qCoWIdBaBBIJb5gtXLWE+8+Hz9r1PGtbC+NHEg2ERAgIgUIEMFUIbVshvLbpQjjlz2CLWRJb5FUYSzau+vb9fMUZRdONlluSjAAL2m699VZv/rJ8+fJohzZ2ZkMRNH/+fEcYiRAQAulHINGkIfwQpL8Y9c8hGl5tLVx/XBWjEDAEmmXDi6mCbThB2qy+P+WUU9ykSZMKdjBjx7UwnOUzS8eBAwdEZhrkO5yp4zqcPcFjA1puSWkEWOyHz974gjaUQOedd55bvnxFrt27lUZPT4VAehCoSMOLHROO2rMu+J1ktXY7CraIU6dOzbzGBrMTmz5sx3pSmZqLAPa7r75aaJuLCYOZMYS5wSwITfCQIUPC25k6jxNYNNm4W0NrzXlIgFFqHHbYYZkqX6syC34saDv66KPdkiVL3L333uvbEN+TuXPnOMxhWNSG/a9ECAiBdCKQSHjDjhFbpZkzZ6azBFXk6qabbqqY8GbNpAEH89TRcccdVwUi6Qv6zDPPOP6KEZL05VY56g4CzbDhRaO5YcOGgmyar1Vu8tzaGtrdHTuy7amhX79+bujQoZHmGq01Zhq43dq7d6+D1Jscc8wx7gtf+IJd6lgBAgyGbrzxRr+YDRMyCC/th3NMYjB1GDNmjMPjg0QICIF0IZBIeOPZpMPMk2Rt0Rp1gxYi6x2tzEjy9CtrfFnjG04wW4WGztrZ2rVrvYmDkV60wWiFs/o7op9mNo7FVciOHTvc+vXr3emnn+42bdpUYLLBbnNZLWfjW05yCmA8a9Ysd9JJJ/m2g69ehCPu7+bMmeOVD3n7ZiYjpidCIB0IyIY3oR5Mw5vwWLeFgBDoJgLNsOFlwVpot/qVr3zFa+DYvYy/0aNHF5QCk5qsuSMMC8Cgd+zYsZEdL0SehVXXX399waJWtNzYMEtqR4D2g2ecBQsWRHgzS4CN+CWXXOJeeOGF2iPXm0JACNQdgUTCW/eUMhihaYEymHVlWQgIAee8VtO0twCChjfUamJzGW7GgJ1/1v1Zf/WrX3VnnnlmVP9Mu0PCwvULbJkbmnZEgXVSFQK0Hxa03X333QU+e21BG+48s96eqgJEgYVAihFIJLyhDW+K89+wrGlr4YZBq4iFgEegGTa8NrVvkIe7q3EPssvOayaYAOzatcsuU38sttEEXgWuueYaN2HChKL5nzZtmrvssssy7X6taMFadBOt+tlnn+191YOtbWbCAOOKK65wP/rRj9piF78WwatkhUDdEEi04c27WzJtLVy3NqaIhEBRBDBpaCTpRbMGwTXiBxFklX0oPXr0cJCUUMub5s0bsLs1H8KUI07grWwsrsJ/LO7XVq1a5b0zDBo0yI0bN85pwwRDqb5Hw5w6Wrp0qbeXNhMHFk6y2E0+e+uLuWITAtUgkEh4q4mkHcPKD2871qrKlCcEINRoMk3QxMXNlLiHO79zzjnHgnmPBtFFik4wxcATC4TVpNTCKAjYdddd5zHALpmyQ/oljUMAfC+//HJ38sknu5tvvtkvZCM1W9CG6zLaW6l6a1zuFLMQyDcCIrz5rn+VXgi0LQIQxEoIHuQjKwSk2rxWikHbNoIWFAzMWTg4ePBgr9U1n71oeyG8mDrMnTs30/6eWwCrkhQC3UZANrwJEMqkIQEY3RYCdUKgkeYMdcqiohECNSPAgjbMGJYsWVqwoA2fvTNmzHCPP/64FrTVjK5eFALVI5BIePNuw6tFa9U3Jr0hBKpBoBluyarJj8IKgXojgEZ++vRp7r777vO24hY/iynx5fuzn/2swCbbnusoBIRA/RFIJLz1T0oxCgEhIASEgBDIHwIsVmMRIT57zR2cLWibN2+efPbmr0moxC1AQIQ3AXRtPJEAjG4LASEgBIRA1QhgT47P3jvvvNONGjXKv4+PaPnsrRpKvSAEakIgkfDKD+/+Liu6a0JYLwkBIVAUAdnwFoVFN9sYAbyCjB8/3t1zzz3exCH02XvhhRfKZ28b172K1noEEglv3m14peFtfeNUDtobAdnwtnf9qnTJCOAy7q677vKL2szEAW0vO+Lhem7NmjXJL+uJEBACNSGQGrdkOInHpmnv3r3urbfecr169fJO4vv169cyl0Fxn501IZzBl8KtMCEluNkpJYT/4IMPfD2hwZAIASEgBIRAaQToKy+66CJ30kkneaKLr15EPntL46anQqBWBFJBeNetW+fwVfjoo4960muFYeTL1A8+DZstedx4YsuWLe6RRx5xb7zxRgQ3TtJHjhwZXYcnkNw//OEPXhvBO0cddZT70pdGuK9/fUJF/k/DuHQuBISAEMgbAigT6F+XLVvmfvGLX7glS5Y4NL0of7773e86vDmwTTQaYYkQEALdQyCR8DbDhhfNYEfHr9211y4oILrdK1J93s6TH162Kv3Vr37l/3CKHsrxxx9flPDyDrs4mVN1e4frP/7xSffjH//Y4YdSIgSSEJANbxIyup83BOgrr7rqKse2xD/84Q/95hRgwIK21157zX3ve99zEydOdJpBy1vLUHnriUDLbHj/9a9/eY1uKbLbu3dvd+KJJ9azvBXHlQc/vNTBAw884M4880x35ZVXRp1sOZAYqNx2220OB+poI0KxVcc4XA9NI8IwOhcCICAb3vZvB3lSHHS3NvHZe/bZZ/s+edq0aVF0aHltQRuKBokQEAK1IZBIeGuLrvK3GLUykmXqxgQTBvwU/v73v3e/+c1v3Pz5V7lDDz3UHuvYAARYHEGHWo1s3LjRLV++PHqFeqODtsUXPMA8hXASISAEhIAQqBwBzBfw2XvzzTdHfaotaJs+fbrDBFAiBIRA9Qi0zKSBqe9w+tzsdceMGVN2kVT1xaz+jTxoJrAfw+YWTS34M2UGSS1FgNEKs6giHKjMmTPHXXrppX7XIDTFCM8Jl2T/W32N6I12Q6CUSQOLV3fu3JnpIsfNwrJenkxXRsYyj8/eyy+/3J188snehtf6ZPrU7du3e2XQlCnflolDxupV2W0tAomEt5HZouNftWpVQRKQprSQXTKWl0Vrw4ef5GbPnu1YnDZ8+HC/atg614IK+vfF+++/7zZs2BA9gihPmjTJd7x0zviVNDMHwkGQy3l5iCLTiRBwzrcfZn+y7hoRbzMmzIjE+zx7lrXjSy+9VDTLvXr19N51ij7UzaoRoN9kwTYLt1nMZuslUBTNnTvHPf/8c27u3LkVL2hjUfILL7wgW+Cqa0IvtAsCiYS3kR+brVu3urDTZGHUGWeckSpilAcNL434uOOO8/a4dK6Q03Ly4YcfurVr10bB+vfvH3lk4HzgwIER4UUTsXv3bi1ei9DSSYgANrym5Y3/3sLZn/CdrJ4z4xHOimS1HMXyjQvJnj17FTzinqQ+CGDicMsttzi+k8zG8dtAqcA5M3KLFi3yO7eVWtCGRx3CEZ5tjuX1oT51o1iyhUAi4W1kMV555ZWIFJHOoEGDUvkDhNyV6kQaiVGz4q5W+xqflj3mmGMijCAt4UBp3759flpa3hqaVZvZTQef16eddpr3wc0sgiT9CLC+AleEcUnyX96jRw8/wKm2z4nHn8frJJ+9zMbNmDHDMUN6/vnnR8qHECMUGWiHH374Yf/dxfXkFVdcEQbRuRDIBQKJhDduf1YvNPjx7dq1qyA6Os20EUtMGrCjkhQiUIyM8CFD+NDFFxkyaJAIgSQEPvroI/+I3//FF1/sJkyY4ElvZ+d+t39/p+vs7PTPzUyGC+x7Ja1FoE+fPt58ybS7Zs5Qrh+XZ47a642BAmsiMI/BjSS7svG7YOaAtRObN2928+fP9xrcMJX169f7Hd3sN/Tggw8mkuPwPZ0LgXZDIJHwNqqgTGFm4YOFtjIPGt5q63nbtm0F2vlS70OOs1DXpcqgZ41DgA84gyUjvQyYmA2ATOHSDrLLb9DOIcEIRNiEMJAuI8Z2X8f6ImAYh6YKZsYQkl3qkDAQX/pQrsuR4PrmtP1jQxHDIuEjjzzS/eQnP4kWGeOzF5MF7N/NZy8zchDj0JwGc8Lf/e4xN336J67P2h81lVAIOJdIeMOp6XoD1SjtcT3zKQ1vPdFUXEKgcgTi9ryl3jTSxREibNfFzovdI267zxGpV1zl8pKltCyvHqB//4PoIkZw4+f/DlZwkDlDARw1XzCIwGcv9rjY5pq5Ava9+OxlETIL2jBlwLNDKGh6Ozru146YISg6zwUCiYS3kaVvJJluZL4VtxAQAvVFwKa40fKaJtDMYOw6nqJpG7mP1hfixbFv374+aNI5D0uFtec+kgrD5iUtw81I7gGsDixMo57Q5IZSzaAlfE/n1SHA4rM77rjDL1pjsx80uRBaFrQ9/fTTbseOHUUjZOHxX//6V+8FomgA3RQCbYhAIuFtlBaWDxxTlqE0Kq0wjWrPZdJQHDGm0ULXY8VDHbiLPS+eGyRCoBoEjDwxyxKSXu4bGbb4bIrdjtwvd17ueRhHubDdfZ6ltMKyWr1YXdkz7hvZ5ZmFw3TFBjdWdzrWBwF2aLvooovcSSed5M0XTKNbytMJpBh3Z6NGjYrqqD65USxCIL0IJBLeRmWZKa1+/foVRM+ua9gapWk1v0waCqoouihGYE07BxkJF7WxNXR8cBNFpBMhECBgtrwQJNuSGuIUJ732ihEtu+ZI+zNSbEfu27m9E4brzvNiaYb3wnMj6rXmJR5XvCzx51zXOy3ijEtIdHmWRHbj7+m6vgjYgrZly5b5BWrms7dUKs8884y3+dXmQKVQ0rN2QiCR8DbS7ODYY48t0BJiRM9oNE2EVxre4s38sMMO854YbMUvgxUIClqG9957z4XaetqQPF0Ux1F3DyDAh5o/I7ncNa1gMYxCQhx/buQrfiSc3QvPi92r5nk1YdshLcobSlhPRnR5DtEOn9k71LOksQjwDcXrkfXPpVLD/AFizIZDxeqr1Lt6JgSyiEAi4W1kYQYPHuyGDh0arS7lx7l06VJ36qmnpoogmRalkVhkLW7MFNgRjxXBCLtJvfvuu77eOA9txuhIRXizVsOty69pecmBEVvTUoaEqnU5VMogQF2geY/XifWXIXmiTpHwnlBsHAJr1qzx39JKU8DOF+KrjSgqRUzhsoxAIuENNXX1LiCj0MmTJ0eEl/ixO5o3b553oh1Om7fqh0iHbh14vcuf5fjQ5A4bNiwivHSWbHs5c+ZMd99990WaBex8cY0jEQKVIGAE1wiSmcnwrj2zeIwE29Hu61h/BJIwZiBrpiGkWozQWl0We1b/nCpGtg6+5pprClyQlUOFmVVtRFEOJT1vFwQSCW+jCzhlyhSHA2x2ijFBa4h7FbS/EG52YOvo6PDT5RamWce49qJZ6WYhHbaBZptLWxTBtNijjz5a0NGOHj3aa+yzUB7lMR0IhMTWyJL56LVndoSIhUTK7ltJqr3mvWrfaffwhiVYh/hYueObzFh4jtSfFqmFiDT2HJMgFA/h97TSFLURRaVIKVzWEUgkvI204QU0tLz4D8RXoBEn7mPeEP5oWQSFVrHZIg1vMuLHHXecmz//Kjd37hxfX9RZaDN2xBFH+J1/ZM6QjKGeFEcAMsVujGxQg0CcIL1GgLlHGJOk83i4Sq4rCROm187hDXfKGIqV3472LKwfI7qy2TV0Gnvk99LR8Wtvj1tLStqIohbU9E4WEUgkvM0ozNixY91dd93lXangFzAkTZY+C6HStJjN8pXnIx+yKVO+7R32m+9HwwPNLzv9ULcSIVALArQv/viQhxKSqvC+zhuDQKV4G8FlkMJ5sbprTA4VKwi8/PLLbvHim4p+PytBiO+uNqKoBCmFyToCiYS3kTa8IWi2N/jWrVsdpPeNN97wj3Fnha0oK05bIXk0aeBDdckll7hvfOMbEeQjRoyIzsMTNDz4fhw3bpx74YUXHFsO46OX8BqghEjpvFYEaI+IHS0epm8hVkaw4kcL18hjPM1S1/xWwjzHr+NliT+366SjvW/P7boRxzimYd0kncff0XV9EcBzDjNue/a87UkvfTELiZkd5Tu+b98+nyDXxZRKPOTbu379ejd+/Pj6Zk6xCYEUIZBIeBtt0hBiwNQ3f2nyB5hXk4Zq6oAPHIsKW7WwMGxDOs8PAhA7xAhW/NgMJOJplruO5zl+HX8//tyuk472vj2363ofm4Gt0qgOARQM06dPK3iJ2REWFUJymSV955133N69e92ePXtcZ2enJ75co2AyYsw6DGbmrA0VRKgLIdAGCCQS3jYoW7eKkEcNb7cA08tCQAgIASGQCgQY6LD2hb+kGTdmHT744ANPjM3NHLMCEiHQrgiodZeoWUbIGu2WAEiPhIAQEAJCIJMI8G3T9y2TVadM14jA/0l6r1k2vEnpt/p+Xk0aWo270hcCQkAICAEhIASEQL0RSCS8zbThrXeh6hEfJg1oeCVCQAgIASEgBISAEBAC2UYgkfBmu1j1yb05XK9PbIpFCAgBISAEhIAQEAJCoBUIiPAmoI5Jg0QICAEhIASEgBAQAkIg+wgkLlrLuw2vvDRkv3GrBN1HgFXc5tpo48ZNfrORqVOntmT3w+6XRjEIASEgBIRAXhFIJLx5t+HVorW8/iTyXW5cFb355pvRZiKbN292r732mmP7UZzWz549u2Cb33yjpdILASEgBIRAVhBIJLxZKYDyKQSEQG0IxLW3zz//nHdEv337dreAA/oPAAAgAElEQVRjx44uuzIdcsgh7ktfGtEyV0a21TA+RiHmoUulSq7xMWobMRA+vA7jBs00XrObm5WZ/FVzTZkqwcjiryQ8eTA8a2uBeksIpAOBevzeq/k9xn+/oFDv32ex+MI+L819YLw+6tVKEgmvTBp61gtjxSMEUoEABJedlbZs2eK3gkZ7u3HjxqLktliGBw4c6IYPP6nYo4bds44v3LqXc8Q6bHtW7tres/DNvA4BsnS5F55Xck0ZQ6nHNR9By0c5DIs9Jz8WB0dERDisJZ2nEYFifQv5tN9C/LyS63r8HkOswt8b98NrfmvVXlvZONpv1srV6OuwXJYPSzv+LMyLPaNPoc6607ckEl5LJK9HmTTktebbs9zvvvuuu+6669zTTz9dMcGNIzF8+PCmbSMd/3B89NFHjr+42L20Hy3flk+7TsMxnie7rvQYlsE+pN39MIVx6lwI1AsBI7nEF5Iuc0Fqbb5e6dUjnnie7DprR8PC8m3XxY4WhmOPHj18EOtb+DaEM1HF3k+6l0h4827Dq0VrSU1G97OIwKGHHuomTpzoCS+2uLXIqFGjau5oKknPPkbhh8g6Pns/7Ozs3MgxnWDSPd635xZX/F41zy2sHbsTl+WnXFzlnls83cmLpWHHYnGF6dh5vJ5M+0Q8EiGQBgTi/Uu8zcbzGO9XeB7+Lix8eK/YebF71caVl7TAKi7xerK+hWO12t5EwhsmyjTomjVr/MckvJ+1823btlWV5SxtLYzN5aJFi1zWByqY0uAVQFJfBOgYxo8f7zo7O92FF17YxT63XGpHHHGEGz16dLlg3Xr+8ssvu5UrV7q9e/d2Kx693BwEjjrqKDdp0iR3+OGH+wTtY8UHij80MzYAaU6OlIoQKI7Azp07XUdHhzfpKh5Cd9OGAN6ABg8e7LNVrG9BMVI3whva8N5+++3u3nvvTRseVeenGs0WJg3WkVedUAteoGwrVqxoQcpKMksIfPOb33Sdnfvdtdcu8N4YKs370KFDHQSnUYJ98eLFi9WGGwVwA+JlESNy/vnnF+xKGX6cRHobALyirBqBRx991F155ZVVv6cXWocAHPTHP/5xQQa627dUpOElxWrIYkEOM3phWwsbwBkthrItBAoQYEQ8Zcq33Z49b7uFCxdW/LseN26ca+TOg2j1cX8myQ4CfBP4M/tHO1IC6zdN02vTydVqZLKDhnKaZgR27dqV5uwpb0UQ4HuA4tG4mAUp1rdU2q8kEt5wavz44493gwYNsvQye8SXKD5GK5GsaXiZcsZGs0+fPpUUL7VhmM5mNiFvA6xmVggdxsyZMz3GS5YsKYu1mTNU2qlUWxYjQ+F706ZNc0ceeWR4K3Pnq1atcs8++6zP94QJE9wpp5ySuTIUy/Dy5cujfpTfK32lSfhxsg8Tz2zBiYXTUQg0A4FifQv9GTNWWZeQz7RLmXCJ+eKLL0ZVY3bU3CjVt/C8ku9TIuGNUnTOnXXWWe6yyy4Lb2Xy/LbbbvNarUxmvkyme/fu7ebOndu0VfRlslPz43Xr1jmmn0R4a4aw7i/ycWDQ2wjhgxQuUiMNpspnzJjhxo4d24gkmxYnZNAILxryK664omlpNzIhiHyoOKCcvXr1ipKMf5hMy1uLzV0UqU6EQJ0QQDF0/fXX1ym21kWD1x3MTZHp06e3DUczwotJA2tOkoQBddi3EK4c6U0kvKENLx+gLNmzJgFUzX3rtEMtRTXvK6wQSCsCuChj8FeJdpcyoJk8+OCDG1qcUEvY0IRaFHm7DOCKaczMlAHSaxoZ6z+B2/pQVlVLhEAzEYgPpkmbWdB24DPx2dx2KJOtC7A2Yn2LXVtfYveN9FZqbqceyJCMHbNm0hDLvi6FQFEEzB9vaDaCuzHEtJHhi3RAjdS0FvsghenrPN0IoBgxkktOTdNL/wnpNUETg5TTwFh4HYVAdxEI22U8LgZuWW6LxQae8TK2w7XNHhnRtTKFfQv37DtSrk7/j0UQP4Y2vPFnebgONRR5KK/K2P4I4Jpn3rx5fhrMNI7Ylt5zzz3eZQ/nccGc4cQTT4zfruu1kaG6RqrImoYA046QC7Qudt60xJWQECiBgPqWEuBk6JENXkITB/obu09RKplBkoa3RKVXqiYvEYUeCYFUIMB2wpDdxx57LMoPC8OuueaayO771ltv9T6QQ03vaaed5ti0QiIEkhDAzV05MQ0NHyg7L/eOngsBIZBvBKxvYeYIggsnM5IbanltYFOzhje04c0j5O1uU5jHOs1rmV944YWiZBeCO2TIkAgWzu+8805nJg6YM3BerhOJIqjxxLaOrPF1vdZCBDCRQfgwVaLdrUQL08LiKGkhIARShMD+/Z0FfYvZ7taaxUSThlojbJf3wtFDu5RJ5cgfAni9mDVrVqTZhcTOnj3bQXaLLXI44YQT/I595uamUd4ZqAkbqdvoPH+10x4l5qMUStJHSfUcoqRzISAESiGwb9++Uo+LPitn25xo0pB3G140vDJpKNqmdDMDCPDDf/LJJ938+fMjv4aQXVzXXXXVVSW9LowZM8ZretmKe8CAAQ0rLdo+W2zQsEQUcaoQaPRsQaoKq8y0DIGQ+Gig1bJq6HbCoc2uLYi1e3GlZCWzR4mEt9s5VQRCQAi0DIH/+Z//8X5fzV8qZHfBggV+w4lyLsYgJePHj480sC0rhBLOBAL2AerVq6c3ayhlowsREenNRLUqk0IgVQjQz5TqWyrJbKJJQ95teOOjh0rAVBgh0GoEMBNYvnxFAdnFPOHGG290l156aUnNbjzv3e1c4vHFr6XdjSOS7WtbYEIpjARnu0TKvRAQAmlFgFn40Hyqku+JNLwJtSmThgRgdDu1CEB2cTH2/e9/P9qpDrJ7yy23uG9+85up06zJpCG1TUkZEwJCQAhkAgG+e5UqZxIJb95teKXhzURbVyb/jcAHH3zgbrrppoLd01hwtnjxYnf66aenjuyq4vKBgBQH+ahnlVIItBqBSmx4E00aWp35NKQfqsvTkB/lQQgUQwDXUHGyizuxu+66y9viymayGGq6JwSEgBAQAmlGwDZIqlceEzW8ebfhRTNRzG1TvYBXPEKgHgiwe9oPfvADt2LFiig6dkzDZhcXY2mWSmyu0px/5U0ICAEhIATSgQDfk3Ja3kTC2wyTBlbs7t6920EuQznmmGNaPgWLSQMa3kptQ8L8t9M5dVSKmNDApEFsTY2ze9qiRYu6kN34hhKtyV35VJthw4t9FzvHvfLKK34hVagxwHPFkUce6UaMGNFQ92vlkag9hH6ftWOnN4VAvRFAARHnM6yjyDuPqDfOtcaXSHhrjbCa91577TW/A9RLL70Uvda7d2/3+9//vuUfIGl4D1TJT3/6U7d69eqi2m6m0i+44AJ39tlnR/Wnk+YgwO5pLE6LbxX84x//uOW/neYgUFkqe/bs8dsn08eEZNfehvQOHDjQnX/++f4va7M6+n1aTeooBFqLAOsoZs6c6UI+079/f79oeOTIka3NXAWpo0Dhu2LeVj7/+b5u7NixRck6Ydetey4x1oEDBzj8uTdTGVZOu0tmW0p4X3/9dbd27douH6KtW7fqo53YlJr3AO3Rgw8+6DVkSamOGzcu6ZHuNwgBdk9buHBhAdll97Trr7++6MCkQdnITLSYZxUjuxSA+/xdeeWV/nj11VcX7eDTWFj9PtNYK8pTXhF44403PNk13+fg8P7777stW7a6NBNetNIdHR1dvvUsek5SPt57773+G5RU15jVNWNb+jD9bpk0NMOGl52cin2I/vKXv/iRRViYZp/LpMF5c5NmtINm122W01uzZo3XWDJNj9juaZdddlnmyG4pU5lG1RF4TZ061fXp08dt2LChYMC9fPlyR0ed5o9TiAvmYPp9hojoXAi0DgG0nhDcUOA3r766NbyVmnM00g8++JBbtuzOkkqteIYZaO/duzd+OxPXiRreRtvwYlv34osvRiDxITLyu3nz5uh+q05k0uC62CIx4ou3i759P9+qKspdug888ID74Q9/GP1ujOyW2yo4rUA1w4a3WNkhvJBaTHKmT58eacrRzGzatCkzhPe9994rKJ5+nwVw6EIINBUBeItxmJDPoNiDXJbb4bKpmXXO7dq1y82dOyfKc6Xpo6hAm51FSSS8jS4M+1tv3LjRJ0PjGDp0qJ8OoMFg21uNM+FG5TXvi9beeustt2/fvqiO5s+/yo0bN7YA7kMPPbTgWhf1R4DfwqOPPlqwexq/GTwxzJgxIzNT8PVHpnsxYq+LSU5oB81HAA1GM23Pai3FO++8o99nreDpPSFQRwQgtBBbk9GjR0f9CnyGfmXIkCH2OBVH+j/WL8C5WFgHBytmYloss6Eme9q0ae6SSy4pCMYMWiU2tQUvdfOikvRaRnhpAGbcDWkaPnx4dM00HdMDrXSrhEnDQQcd1M0qyPbrTFtYwz5QRyfJtrrJVQrZZfc0yK3ZhtE53XDDQved75yXCWLWZMiqSq5Xr14F4RlIZEX0+8xKTSmf7Y4AfMYUeJSVgbSRR3hOGk0A8Bxx1llnua985SvezIt8jx8/vmxVsRA4NKXCVjcNZmCV2PAmbjwRFqgsAjUEYDWgCZ4ZvvSlEXbpduzY4QlvdKMFJ3HXIi3IQsuTpGHbFA2ZOeyww1qepzxlAK3Bz372M++NISS7bBXcDmS3FTa8YfthMGG20NyH7J588smZGUTo9xnWps6FQOsQgNDCWxAUErg7tNlPvqEsXEuboNBj7Qffk2oIK6ZU4cxv375901a0xPwkanjjtpqJMdT4ILR3GTRokBs+/CTfQGgc/NGZt1K0tbDzfkutDtD0omU86qijHNM1aOTlW9DQqf8R+9Lbbrut6FbBlYzC65+j+sfYKhte2jID7pUrV7qHH344KtiZZ57pTj311Og67SednZ1RFvX7jKDQiRBoOgLY/ptyCFdk9meKChaupc1UCrOtWtwwYupoM78ADZfDhOHEE0+sKb56VVZqTRrQrIT2LpAo/mgk1kBY0Ea4VpKqPNvw8uMMDdP5Md9+++2+bTKCnThxotc8DhgwoF7tVfH8GwHI7nXXXedw/WKdKNNG1Y7EBWhXBMBz/vz5/gEaGa7R7LKQDb/GaVtY0rUEB+7QN+r3mYSO7guB5iHAtzJcgM/GWXwXOdoMEh5hmNHKwtqAcsiFA236zyVLljg83MDfJk+enGp/5oka3kaaNEBqMeQ2gez26NGjoIHQmTOl2yrCm3cvDZD9pDZA/Rn5ReubFZJg7S3NxyxvFZxmXMO8hR8n7rNwA5OqLE3NsehXv8+wVnUuBFqDAEQ2tN9ldpy+JJwl3759u+M32yo+U09k4qZUkF7+4AUQfLhbK3hBt2x46wlQPC5U4vwhaFewd0EdzdGEBgLpapWYH95Wpd/qdBmAoAnD8fRvfvMbt2DBAofbo1DQQP7pT38Kb+m8GwiwUPMHP/hB0a2CW7mAsxtFKvlqq2x46XP4M4EA457nRz/6kXdVZvfTfOT3ycrocr/P9evXp7kYypsQyDwCED3jMxSG7yTEFkWeCTav4YyM3c/iEZ7GRkf84bc87EspD7zgD3/4QyqLlrhoLRyd1DvnO3bsjEwXiBt3Haj6v/jFwVFSTDei7WqVoOHNs5cGfrCQLOxF2TqYKXb8wNLATRjV4S6L6VVJ9xCA7M6bN6+A7NKhLFu2LHXubLpX0k/ersTm6pPQ9Tmjc16yZKl7/PHH/SDOOmubmsOulynKtAu/TxaalPt9slGJfp9pr03lL8sIsB7AbFrpT0xxx9H6F55v3Lgpy8WM8n766ad78zpM7NiJlb4UkzsT+tJf/vKXTe93KvmeJBJey3y9j3S+zz//yR7MrGTkY0/HHO5IAmgYgkvSgQADEgYmF1xwQUGGGLUyVSOpHQG2CsafbugP1rYKlo107bgmvTlkyGBPFtlGmJkLE/ocTHXs42X3s3C03+eUKecWZBfbQf0+CyDRhRCoKwLhAnz4DOuT4DPhOiX6lpDf1DUDTY6MvoYBt/0x8F60aFFE7skOrtha7XigGCxNt+Gl8w1V+0wHXHjhhT5vNIpQ4rZ24bNGn5tJA5Uq+QQBtL6MWq2uIAcMYmTH+wlG1ZzRMc6dOzda9AC2XGd197Rqyt4qkwbLI7/tSZMm+T3hrT0zs4RPzVpWL1u8rTyOHDmiy++zlflR2kKgnRHg2xcSW/gMi19NrF/hmnCEb0dOgYcGNq6wRXqUF/dlzVTYpNKGF4JkG06EjSJsGHYfYkwDaYXk3aQhCfN4fTCibccfcFL563WfaXNMROJkF41jHshuvXDsbjwMbM1fpsXFDmZZlbiD+3jZslou5VsIpBEBtJjhAnzyCJexvzDPhEuj1jPMY3fO431NGnlBooa3UTa8aG3DKUMMvPHDixgZNvLLwjVGTK3Ykk8a3uJN/4knnoi0u4Q45ZRTcm3rXByl0ncZNHR0/Npde+2CyJYdzS4rW/O0VTA2V63W8sb7I2oO9zpZFdvdyfKv36choaMQqD8CrDMKFXi2Ra+lxDM4DMLCNsI3U+tp+Wj0Mdw5l7TYTKxfv36NTrYg/kpseBMJb0FMdbxA5W2ElmixVTznnHN8CnhlCFeps7KRRtIKwkuG8rxoDV+wTz31lBsxYoTHgbpZvXqNW7p0adQaIGkYq2PTI6kMAcguWwUz7WW/AzpJFgDg2ziNo+LKSpatULg8ZAaJ9mz1QAnYVKXZHXUtyPHhfO655wp+nywg1e+zFjT1jhCoDQF2UAv7j+nTp/vdyyw2FnubC0+Ib6jsszBZOvL9Mnex8CMUFmitWVxtxJ7ysF1xGvlTIuFN8vHYncphGje0d4EwHXvssQX2crbCkXQA8JVXXnFjx47tTrI1vZt3kwbILrbVTFOg8WLgwY81/HHjrP/LX/5yTfjm8SU6CjqGhQsXRjhCdu+8886K9jBvN8xaod2l/YI/EmpfuKY/YlFmFuzRGXziSk2/z3b7Vag8WUEAPhNfiIYCKLT/j7vypM/J8k6ZcDK8CSHMHmFChQ/i0HaXbxrcoNmKsEpseBMJbyMa3e7duwvsXTByjk8f4pqMD48RK1TlNKxmg4dJQ57FVp5SD+HIzTDBPVmWdqayfLfqCNm96aabCrYKpnNkdeuYMWNala1cpht6wzAA6KTnzJnjvva1r9mtVB/50PLb1O8z1dWkzLUxAhAsvKCY0IccffTRdumPKPRC4buKljSrM3kQXDObKtaPwt3oR4cPHx4WOzXniYS3ETa8TMOFDprRTsSnDwcOLNyqlgaFZjEcNTUDvbxreGm4jE5t+1Uw5wdt2wdOmTKlLW2RGtG2aPfY58a3Ckaz244bSlSKYbNseOnLwkE0+eOa/gdbMzpnbKcZgGThQ4QCQL/PSluZwgmBxiDAVH5ov8u3Ma4o417Y96ANzfKOayF/i6NK/zlz5iw3Zcq3W9KPps6Gt0+fPg4bF5Nhw4Z1mT7EvQUr1U3DCyHOwkfIytQux0svvdS7bHr99dcde2d3du53DEb4AbfKpjqL2EJ2Q7t0yoB2HAKcZ7LbrLpki0/soxk0v/32nijZXr16Ovoj2jN73jd7BinKSA0n5DXp9zl48GANRGvAVK8IgVoQQJtpXAU+E9+eHEUdfT0z1QjkN63CYro490IpEApreu6//35P9FkD8f/Zex+YK6pz33/l9JobRVJBMRGEgogm4p+ioRJtrOLBa1CpaCyNCrE9ETwiKCKGKCqKGiN6PYB6VNKrAdrIz6gVqVevBStRgyUUqXKSYqkURBOpaIPYGHOSXz5jn3HteWf2nv2+e8/f70red2bPrFl/vmtmre961rOeB9VX1FBZmcckYtG5QaKEtxs6vICBEreFuEGGF+SGG26wKMExLl5DhC78iM7UupBFoZNkkkF7Ff0FLjKIeOBB7cNf+pk6dapbsGCBcHUuEwsNvMcYRq9a0PdZtRZVfcqGwFFHHdXAVeJ4CnzmmmuuafDeGBevCHWH8LbiXsThDz1kVDOQVrM5rQh1KpwOL42aBpg0cbr9gtRdpaHb+FY9/Tiya97TslbPqTrWqp8QEAJCIGsE2uEp7cTNuh5+fu2Uk0k3f2UKiRLebujwlgmYukt4y9RWRSsr3tOQ4trOVZax2LV65513Zq6LXjRs/PJkpcPr56lzIdAKAXSk2WCNpygGdNTqzHIHUi10NxGI+NdJE1OO/DF2IP2DPMTFR82JP1Rq2BdhpMFM5ZEWaWti3KqldF8IfItA4XR4vy1aOc6wPWudUTlKrFLmjUCdXQXnjb3yFwJ9RQAi+sQTT7h169YFG6zZ1IhNUVSTWMo1s0zohGNhxTeZ+cwzz7iVK1cGOuEPPfRQQFghsehwslkJ/Uj0yB9//LEw7auuusrNmDEj0DEnHraUCVgwIn4V1XH62kZ6Xgj0FoFECW83dHh7W8g8nmMGrxl2HsiXM0+kQr/+9a/d3LlzQzNuZuqKAc0kROWsXXdKnYcd3u7URKlWAQHIqW0wNYs0jIOQVTOij1kms98cdePMxiRWdaJjJ5t7uI5TEJ6FRBPw8mc2ubFPv2HDhuAelnHQ+4dU46RG+yiq8HapDt1GoJA6vN2udKfSZ1lKEt5OoVntdFi2jLoKZsC8665F7oorLk+lt15thOJrJ5WGeFx0NR8ENm3a5NasWROoGUA0MbOE+gJS3+hu9WYl9NUB/RVCSCxSW9SbIMtMjiHCS5cudUOHDg0c0GDH9cUXXwyIMPc2bnxbhLcZ2LonBNpA4F+S4vofbVKcKl/XprUqt27n6gbZffjhhwOvV+agA/vFmMIS2e0czkpJCHQbgT/84Q+BiSnUCXAGA1lFjQG1gnY28ySVEwI9ffr0gMCSJhZbCJi1QrWBne9Ic1kRogyE3//+7YYd/sFF/RMCQqAHAml0eBMJb4/UdEEICIEGBNigAtm1ZUluQnYXL17sLr300o4Mkg0Z6ocQEAJdRwBVAv46HbD37NtpxX6p2WU988wzw+xQfyIuAXUIqf6E0OhECPQJgUTCG9VD6lMuJXzYVBpKWHQVOQMEILvYlPbJLhKc5cuXl9pXegbQhVloIA+h0EkBEDj11FMDAoqeLd82qgydDO2smrYTt5NlVFpCoKwIpBlPEglv3T84qTSU9bXvfrkZCBkQH3nkkdDLDmQXV8HaVZ0e/zRLUOlTU0wh0DcExo4d62bPnh0kwreNasHGjRv7lqieFgJCoDAIJBLewpQwp4JIwpsT8AXPFrLLTm4GRAu4CmaTi1wFGyI6CoHyIYAqwc033xyQXlQN2GR29dVXu5dffrl8lVGJhUDNEEgjQBHhbfJSmCmaJlF0q0YI4D0NqQ+2Ni2w8eTxxx/XTmoDREchUGIEIL233nqrW7p0WaCPb6bDOq3eUGKIVHQhUEgE+qTSUHcdXlQaFISAIQDZxSc6Uh8LuArGwDw7uRXaR6BZB1W1yaZtTmofpWI90QlrBcWqUc/SYJ1h2rSpgbkwzAui07t+/Ws9I+pKaREo+3tc9vLn9eIkOp7wdXi3bt3qVqz4VqqVV2H7mu/mzZtTJ4FKQ5nC/v37gw4ad5VlDtin7MYO6b5gkuQ97frrr5dzkj4Am2SHFzNNjz76aMPkog/Z5PIoBBeHBRbWr18f6nvbtbIe9+zZU9ait1VudHoxT8aKDubBIMH9+vULHUccOCChSFuAFiQyPKBqfKYqdYJrdjMkEl4/Uz54DHKXPTCQthPK5HgCG7C+Xmk79VTceATwnvbqq6+6efPmBV6RiAWRwXi8vKfFY9apq77aSKfSzDMdVgb81YE8y6K80yFw0EEHORP8mCBhwIAB4TVIsNnaxktbOwKVdCVQrG4gwHf45ptvdiPpTNP0+UwV69QumGl0eFMRXjL2wW23IGWML9fCZWy1zpUZsvvLX/7K3X77bQ2ugufPn+9+9rOfBUbpO5ebUhICQiBvBNDT5W/MmDGOwRNPZ2vXrg0muaecckpQvKOOOiqwkcu9119/PZgQm3e0KpCovNsgq/yryGeqWKd23gdU5FqR3kTC6+vwYkx/2LBh7eRdyLi7du0KJXWtCmhWGnzXkK2eyfM+ksdJkyY5jJmXOeBTntWEPD/eZq6Cp0z5ichuh16wZjq8bAYs+7u8bt26gDQBF2brzj333A4hl18yqBxBAs2rYH4l6WzOTHCfeOIJt2LFitDLGbq71JN38eyzzw4yRHfy8ssvD/ooNrSh19+/f3+3e/duh/MISK8/dva2lJ1Io7d5V/25KvKZKtapG+9hIuG1pRwyxe3hdddd1438M03znnvuSU14yybhxRc7S+8nnHBCpph2OrMNGzY4/vIivOYq2HcoQWeycOFCd/HFF8t7WgcbPEmHl8kbUnT0J8scIIdIAgmQXWw3lz0wSUE3uWqEl3Y59thRAdlFMMKeiCFDhrhp06a5qK7+Oeec4+67775Atxd9ZsZK1JwgvPQb0XDaaacF+xJGjBjRcOuwww4LnmHPArrBfkCivGPHDhd9xo+j894h8KMf/Shw/d67p4vzlM9nJk+eHFgXKU7pelcSPJfedNNNvXq4lXSXRBMJbzTHskg6o+Wu02/aqOy7N/PcnY8e3v333x9s/jPCDdnFVTB+7hWyRaDs73IUrarVJ1q/Mv+mbdiUdsEFEx1eFJn4oq+LCkO03ehnsc974YUXus8++yyIh6UWpMSYKCRAZgn0ZxDm6dOnB+f+OIrqhMX3XQ7zHEKmKVOm9HgmSFT/+oQA+tiQo2i79inRjB/mXYsG/92K3tPvbxBIJLx1X1Ipm0qDXui+IWCugv2Nf+Y9TQ4l+oatnhYCZUHg8MMPT2V5BbIEyfVNEto1v65cI824AEHxn/fjJD3jx9G5EBAC3yKQRodXjie+xavhTK6FG+Co9A82qsyZM6fBygXe03AVLLLbvaZvpsPbvVyVshAQAkJACNQRgYb6QOQAACAASURBVETC6+vw1hEYk/DWse51qvP27dtjvafhUEJkt7tvQhqdq+6WQKkLASEgBIRAFRBIM54kqjRUAYC+1iFPfdK+ll3Pt0YA72mYGfPto7Ij+957701camydqmIIASEgBISAEBACRUMgUcJbdx1euRYu2qva2fJs3LhRroI7C2nbqUmloW3I9IAQEAJCQAjEIJBmPJGENwY4LpXNtXBCNXQ5BgG5Co4BRZeEgBAQAkJACFQYgUQJb911eCXhrd5bjymXZ5991s2ePTu0x2yugm+99dbE3dTVQ8IFZpcg/ugw5xXS6FzlVTblKwSEgBAQAuVBIM14IglvedpTJe0DApBduQr+FkBsDj/55JOBcXv0li+77LJaEf5vkdCZEBACQkAI1AGBRMJbdx1eqTRU5/WH3GHgfdmyZaGHqO9973uBt506e0/D9jCewPh78cUX3axZsxxepLIyYJ5G56o6b6FqIgSEgBAQAt1CgPGklZQ3UaWhW4UqS7pSaShLSzUvp5FdXH6aO1S8p2Fjt85kN4oalip++tOfurlz5zqsV8R58ok+o99CQAgIASEgBMqCQCLhrbsOryS8ZXmFk8uJBBOzY5BdcxWM97SlS5cGroLL7Foyuda9vwNGeJq76KKL3J133ulwyNHN0Go23s28lbYQEAJCQAhUB4E040ki4a0ODL2vyT/+8Y/eP6wnc0XAdxXsk10kuyzbKyQjgCScScKUKVPcihUrHVJyBSEgBISAEBACZUYgcx3er776Klha3rdvn/v8888bsDvssMPcwIED3ciRI13e0jdUGuTPvKF5En+w/M3k4NBDD02Mk9UN3hskk7fccotbuXJlmC2ugu+77z55T/snImn0dNHt3bZtm/vtb191l19+ecf1e7PQ4aW/wRLFnj173Cef7HV7934SvhODBh3phg492o0aNarSjkaK9H2G4OukqwiglkQ44YQTch9Lu1rRnBO3/uXAgQOF5jM5w5RJ9ml0eBMJb7dKyOBzzTXXBAOpSd4sL0xEjR492o0ZMyZw95qna1dzLZyGGFj5y36EKK5evdp98MEHYVUuvfTSRIkokr9XXnnFvfDCCw6J6ogRI9wPfnC6u+SSybmRXzr6xYsXx5Jdyidp5TdNG51shg0eOeEbZeKwYcMGd+GFF+b+XUaK1/Ln3r17g/4G8h4XrM/BUsXPfvazzDbsxZWl2TVI644dO9yqVascwgILmNg77rjj7GfDsYjfZ0MB9aNrCPy///f/3FNPPeUmT57spk+fXukJXddATJHwli1b3NVXX+32798f7hGxx6xvgc80+04tftZH+hTGgU2bNrmdO3eGpjrZ43LmmWe2nCzRv7zxxhvBpmf6JHsuT97WCsNEwtstHV5mQkiNomSXgnLNdo2//vrrbvny5W7cuHGt6tCV+3WS8BrRpYN87733GvDkJY5TATCVAQZgvy35jUQwD/e8LMUzmYqSmzfffDPQS22omH70kEg0gwRs0e/lu7zqqqsCdYejjz662SMt76FzlYWUt5nFGetz6JPom6677rrCkV6EBHxXK1as6DGoMiGNI7x8n3PmzHFr1qwpzPfZ8oVQhI4iQF/O37p169yMGde4Cy6YqFXLjiLsgpXN6JhpWVjfwniUN5+xMvlHyO60adMc46M/hkPUWW3n3s033xwrvKJPuvvuu3v0L/CFhQsXBsKRrIWFaXR4EwmvD0w3zwGX2QTBB56XCD1CJI5FWCrvJgZ5po0jhv/9v/93D5LYrEzMDJcsWRIQoGg8PhwkgsOHD3c4c8jypUe6C3GJBsrkf9DR+/qdHgG+y5tuuslt3brVPfTQQ6UbQOlvJk2a5JjQI52xyRHvBxO+8847rzBqL3xnTPr5nqycaVrKvk9fpceey/P7tDLomC0CvDummsQqBht3s+yXs61tvrk14zOPPvpoIAUtEp/ZtWtXj7HRxkv4F/XBco8fmExDduP6F8YH4qOaGico89PJ4zyR8DaTinSyoEOHDg0Hzt/97ncBWGY+io8UAPOQ8tZBpYGBEW9b7QymtD1LIEibLGDT9qyzzgqWva3tuI8zgyyXN4YMGeJ4n+LILWVUaESAGX4cVo2xGn8ZYbz22mv7PBHNQrrbWPpvflF2lhl5V5GCYpKNQF/DpDvLdzaufHYNfLCPXJXv0+qlY/YI2EQHif+VV15ZOtWk7BHrXY6oZNrY+JvfvORmz54V9rGohaEuWJT+hUkP/SCTf/ZNESijjeH8fu6553qs5lEv3iMLjK39+/cPV4d5HqdGY8eO7fMYYXmkOfZJh7dbKg3RgpMPm8P4Q0cQfVCbOTAgs9kkj1AHlQY2eKFzy1I1Ly34+1KvONwhyRAE/6PA9BeSg4cffjiQ/vEc95955plMP24+XkyOPfDAAyGJoSyQNOpGR6/wDQJ8W8zg2yFTSIZYGu2UjnZWKg1xbU5njyrA+PHjG96Vjz76KLBBnPemWcoMPqeddlpQPpYK0ceMU2vw69fs+7znnnuCNic+3yd6nkUZfP066Lx7CEB8O62a1L3SljNl1AHoP+gnV69+Ouxf+Ob+9re/FaZSBx10kJs3b54bMGBAoONN34E3Up+km9DR1NfQ26VOJiiBN2D56Jhjjgk4gI0nEOe8hJXNAE6U8DZ7qFv3GISyItqt6lAHCS8YjBnzfTdz5syADDKoIv2ylzYOI4jS5s2bw1u88JAG2u7UU08NyKV9DMRjFyv3sgpImtl1j0UG0y+mPGvXrg021c2YMSPTWWdW9W43HzouOuY0gfeiU3q7afLLMk6/fv0asmNyVASyS6EoB5OM2267Lfg++dbQx/Qnmw2Fdy7QyyaOBZvI8g2yxMiE0L7P9evXuxtuuKEw9bUy69h9BCAjqCYhwbvxxhvd2WefXTr1pO6j1LsckDTy7aK6UGRLT/QJ/oSXMqPn7ZN0EGBvgwUk1L7aIBJt+hXSOvfcc0PuQB/1zjvvZLo6XwodXgOSI7ohvoUAlqf9BvHjZnF+8MEHZ5FNrnlgtgZ9XF52ZnitAubH/BceNQL7qI844ogGlQL0g9glb7PDVml36j75QXghahz5+Pgz3dMFCxbEbvTpVP5lSIeJSKsA+av68icDvwXqSwdepDBhwgTHH98nbdZK1Yw+1F8V4/u0iU30+2Ty+vHHH2f+ffYF3zTvbV/Sr9uzCDfoz9FrZ5UOgQHvmkLvETDixbeIZRULjEd8j0UOjO/0CxboO/xNsWxW8yfcWD4ygdaxx46yx4Kj37c23MjxR6KEt1XH2qkykw8g83KwaQQdOgs/+tGPQt0Su5bVEZWGOhDedjs3LDr4HwS6P/bCszTiS+gx1fLZZ5/lMqAyu8ZczPHHH+8guCa1Rl2GTgile3Xu8V8TxI+NpEwQurnBJS8dXt5fOm4knKwCWGDQR++sSCH6ffrfV1w5o/bN+T5ZuiREv0/63ry+z7iyN7sG0WWVxkwgEpeJNuOGjj1xQDUtbUDiT7/IMjQ785nk+iQnbTqK9w0CRhrZcO8Lh1BJMl3ZImLF2P7EE080lBlVQP9dwHyZHyDxFo48clDDCi/9CyuJWW3SK4UO7+7duwMnAXRcZqWBAZfBB93QaIdv4Hb7KNfC8Qj7ZNdi2IDKBMGkSXYvT50l3h2WW1Cg93eVQn6RZvB+0bln9UEaJkU+dlpPt1ld89DhZXBHd5nOmL7Hlvexw8vEyFYrmpW7yPd86S7l9Aly9PtkQprn99kOjqwUtWtNpp30FfcbvW6+jeeffz5QfSniLvuitxMEF7VAhCqc078Yn8Eecl58phlubFznDxVE42DEx1kT9oNNoMUKsPWXlt4hh3yrEkY8xn+LA6djolqk8TVRwmsV6vYRcGyTmuWFdAll6qyXwi1/OzJTs8a2a3U/MsOzF7oVFpBj30h+q/jdus8MFRNamEozHUaWZSC8LLtwzPtd61bd06aLruesWbMCe8X+jD7t82WKZ9J+KzNSih//+MfBxk27VtYjxLBs32carBlIkY75ErM0z9U1Ttp3IIoP/QCSSPZBKLSPQJH5TFJtILtMdPxgewf8sQAJalTC6z8TPWf8txWY6L1u/DZVkmZp50544wqHFQDAQiqX1yyzDlYa4rCv6jUkd3fccYc75ZRTAsPYEF06J3YsozdeV7fDSB+QcvOXh/m/IrxvvAv/9m//FmwOK/umRn+DSRGw7VQZkBLhzAYX17iI/vLLbzfSdCqPqqSD1A3nP1FBUrP6mRQS6WRd+4Fm+PTlnpk9ZPNpWbBlYgkJjo6LENiihj6pNLDkl0VgRomEjY6aHaMmfeGIOP1Xv/pVLhvX6mKlodttHN0F3+38mqXPcpJ5puKds46IIxvs8vIQ06zM3brHysVFF10USHV/+MMf5rLslIcOLwP70qXL3NChRzucrviWPOjgBw060l1xxeWFXHpM8y60+721Gz9NGboVh1WYuq/EpMV2795PUhFevgdWVH/+85+7//W//lcu/UDaOpUhHnzmrrsWOfD3+YwJ8VCv86WmRagT9vLZcPb7378d6Mmz+okwyMZHTCGaqpcd05a7aKqh/5JUcF/3KylOJ66zaxHA8c7By4Ao3QKSF2y55rEzty6b1gzrtEfUAugk0wSWIaM6vWme63YcLH88/vjjgTk2q4tJ+bAlXORZbKewQWLGpr7zzz8/t0EuzRJUp+rrp4MpPlaOHnzwwWBSbffo5B9//LGGTZl2ryzHQYO+2TiSprxF/T7TlF1x+o6AWbGB0CAIKJKuZd9rl08K8BkmzPAZ7NP6fAYhHhPsNNaQsiw94+G0aVMDa01PP/10YN3I8kenF0cTBPrrZrwwusmf/iV6zdLN65hIePMoEDMfNo8YCaEM2JREL02hGAjEEdivv/46KJztTrWS4n0FF4NFDEiKWK7hj1k5AcKDlA/vW7gprnoo4gaKLDC3ZX+k3Khy+P0NS3k4nyhriJo98lfqyvR9lhX/MpSb/o7ldVY4rrnmmlB6V4ayl6GMtnIFkbzkkksaiszGMLvfcKMAPxgPULnA5roFxkQkvwgdud9sPEcNlT8LSIOznESlEaAUivACFBIKP9BhI23NOphKQ9b5Fj0/iKJPetmNahJ4TBz5AyyzwcGDBxe2Sibl9FcW+MDRfWMgePnllws3Gy8smCUtGJ2y/z5TjbJYLoiDPO77tAlp2b7PuPrpWu8RYGKHQAkpHvsZira03vuaFfdJVkT9gOqcfY/+9SKdR1dx2eOCeTECe2D88Oc/vx/+RLfeD6ST5ab/NBOJRMLrExe/Et0+j0pzIU156IFIpSG+pSEHvnF+zCCZRIxzTD1ZwNVvljM8y7edI7NWM13mry6w/ATpXb58efixt5Ou4rZGIE0H1TqVvsX44x//2CCVILWolLRvOWT7dNW+z2zRq25uLK3/4he/cI8++mggxavr6k7WLRy1atBMJSDrsiXlF7XEhHMJG8eZJPkrYsQ1FQ3ILwIjAnGijiiS8svyeiLhzaNhMAaPdM1AAwhIU1TqmwVAkvDGo8yLjythCyi4ow+LCgAbDK3teOExWl2WwIeM6TKW+uyDNtNlbHDDKLdCZxFIswTV2RwbU6O/eeCBB8J3lrts4Gl3Y0Zjqvn+ivs+McVX9u8zX1TLmzt6urzja9askZ5uRs1o/RrfHBvX/ID73aLotdL/UUZboYW4YqIMB2B+4B0ySS0rtvSRFnBWwh9p+S7N8ZI7btzpFi2To+HeLLPczZIhFQRgiBIGr9k8ZAFdI5TpDWy7ntWxKC9mVvVNm895550XKLZbW6GI//rrrzdId/koiua1qlX9IDrXXXddYK8Xiw3Uj/fSTJeVyaxMq7rW+T5SLv7wRmXvMHgw0Zky5ac9VBzKhlVVv8+ytUOe5cXayMyZMx1m9tAlVcgGAfjMf/zHfwSmLhkT/f4FPoMzh6JI1/E0ydg2bNgwhxQX1QX2MCDosQDZ9QVcrCBh3ccsOBAXJ06sipmFLZ7N00uulT3umEh4s1JpADBcmEYDgw+G8HH/mkeQSkMy6nSg8+bd7GbPnhUQQkihSXZ5ig+bNi2jpIzJVTPTZdR7ypSf5DYJS26V8t3JQ6WB9zTOPin9DWYQL7lkcmEGpLgWTdMv831CdliZsG+zKt9nHCa61hMB+qgym9frWaNyXGnGZ/geWbEuSoCM219cmRjHEfz4ut6Qdaxqvfjiiw2k1yfJqM/Ql2ZN7BlPWkl5EwlvHADdvmZLyWYXkCXxrEGzOuahN2x55320dqAcvutAv1x0qBh/x8oBOzMZUHkO/d4bb7wxt4mKX8a+nEMaMNezZMmS0DsbnQMkH12l66+/vpSEvi+YlPVZ1LN4N33SZ++46byWxQ6p1cXaImkVCqkLoarfp9Vfx3gE8loVjS9Nfa/Sz/h8pijtYnq30X6RlrIyI3CcMGFCj8ZDkIX6H8F3RcxzjP84DMMrYhFDIuHtlg4vu4hZGmbwwe0sEgvywtwF+iHf//73g+VyU5LOC7Q6SniZXOBpBzerFpKWw/hwsePKcsfGjW8HhrbZlXn66adXxjg8H/att94aKN8vXnx/qOKA6TLMy0AmkvAx/HRMRoDZeLelvOj/09+wgxhj8BZY8j3yyEHumGOOCVYkijIQWfnijuDFYOK7605yAVuH7zMOI10TAlkiwPdn+wCKzGeimDDW33nnncE+G9QYUGcgoNoAaW3ljAipLwIhbPRitoyA+gNCSjheHqGVdJcyJRLebhUYMDDKrFBMBNpxfchHw4vvL3kUs1a9LxXEgaXB444bFdjoNd0l85wDmWIWnNdKRO9rVo8naT+ca1Qh8I6142q9Dt9nFdpVdSgvAmXmMwh06Bt72z/yPA4r+CtLSLTSkEZXrCyV7E0566zS0Bu8qvwMxIGJQNQ7m0yX9a3Vuy3d7Vvp9LQQEAJCQAiUBYE040ki4S1LJbtVzjycXXSrLkq3Mwgwm0eNAakuCv0ElPXZjCDTZZ3BWKkIASEgBISAEOgGAomEt1s6vN2oRDfSlIS3G6iWP010y2+44YbATzo6SwT00THvggkg7BjahoDy17a7NUijc9XdEih1ISAEhIAQqAICacaTRMJbBQD6Wgd8zysIgSgCqDig94SjDewqWkCvl93xv/zlr0Jj3nZPRyEgBISAEBACQiA/BBIJb911eOtopSG/17CcOZvpMlQcMMlCQMUB02X33HOP+/TTT8tZsYxKnUbnKqOiKBshIASEgBAoMQJpxpNEwlvienek6HIt3BEYK5+ImS5bunRZYJaFCqPigOmyOXPmBK4bKw+CKigEhIAQEAJCoOAIJBLeuuvwSsJb8De3QMXD9BWmWZYvX96g4oBHr2uuuca9/PLLUnGIaa80Olcxj+mSEBACQkAICIEGBNKMJ4mEtyEl/RACQqAlAs1Mlz388MPuiy++aJmGIggBISAEhIAQEAKdRyCR8NZdh1cqDZ1/2eqQYpLpMlQc8GL34Ycf1gGGVHVMo3OVKiFFEgJCQAgIgVojkGY8SSS8tUbOOSeVhrq/Ab2vv2+67IwzzggSQq8XFYcpU6YEpst6n7qeFAJCQAgIASEgBNpFIJHw1l2HVxLedl8lxfcRMNNlTz75pJs69VvXi3hnw3TZihUra6/ikEbnysdU50JACAgBISAE4hBIM578j7gHde0bBA4++OBSQfHVV1+V3ulB1WwfH3fcce6hhx5yw4cPd0uXLg0sOJjpsj//+X03ffp0hxqEQk8EqubAowr1SbNs2LMldUUIFAsBBBJlDmUvf17YJxJeX4f3gw8+qMQy7M6dO1PjXDaVht27d7vFixe7skvmee8+//zz1O1UhohmuuyUU05xCxcudO+9915oumzz5s2Bq2I2vNUtJJEn1D+QjD/77LOlhGTgwIFu37597vXXXw/Lv27duuBaeKHEJ3v27Clx6VX0uiMAD8AjZtmDz2eqUie4Zm8D40krKW8i4fWJE25T+atTKJtrYdMRrVMblamumC679NJL3ZAhQwIbvXhlI3DctWtXQIQvvPBCR7y6BDqnJNKLvnOVAqos/CkIASGQLwL0LVXrX6pYp268JYk6vN3IrExpIuFVEAKdRiDOdBkS33/7t39zmC6Td7ZOI670hIAQEAJCoOoItJLuUv9ECa8PzoknnuiGDRvmXyrl+bZt2wLXr6UsfItCf+9733NICEeMGNEiZrFvs6SxatWqYMm/2CXtfenMdBnf1X333Re8k0job7rpJrd161a3YMECh+5vncPMmTNL/y4/99xzoVR34sSJbvz48ZVo0mXLllW2H61EA6kSTRFgrBw9enTTOGW46fOZqtSJ1U4EQL0JfVJp8HV4J0+e7K6//vrelKFQzyxZsiRYTk5TqLKpNPTv39/Nnj279ERp48aNbu3atZUmvLx/mC67+uqr3fHHHx8QXFvuZmlqx44d7u6773ZnnXWWq/LmhCR1hu9+97vuyiuvdGXXa2byZu0K2Z07d26arqfwcSDybLxUEAJlRADB0J133lnGojeU+Y477ghVTadNm1YZjtZbwtsATsKPRAmvr8PLAMTGmzqFsm1aq1PbVKWukNlzzjkn2KAFwTW9MkgSpsvmz58fHKuq19tMh7dq1jqQ4FchVMHSRBXaQXXoPQJsKq0Cn6EefqhCneCa3QzS4e0mukpbCKRAwEyXPfDAA84+eCRoEF6kgvLOlgJERRECQkAIpESg7BO3spc/ZTO1FS2NDq8IbwKkcjyRAIwudwUBZufXXXed+8UvfuHQ7SUgFcQ6yowZMxyqHgpCQAgIASEgBIRATwSSVOT8mImE19fh9R+oy7lUGurS0sWpp5ku+9WvfuXY5GQB02Xo+2KXFuciVQlpOqiq1FX1EAJCQAgIgXwRSCS8vg5vvkXMJ3dJePPBXbk6d9JJJ7nHH388cEhhKg5muuyee+6pjOmyNEtQeh+EgBAQAkJACLRCIM14kkh4WyVeh/tlcy1chzapSx0xXXbrrbcGZsswOUNAxWHRokVuzpw5bvv27XWBQvUUAkJACAgBIdBnBER4EyCU44kEYHQ5MwRQcbjmmmsCKw5nnHFGmC/WHLDi8PLLLzttXghh0YkQEAJCQAjUFIE0KnKJhLfuOrxls8Nb03e8FtU202VTp04NrThgugwyvHz5cvfFF1+UEoc0HVQpK6ZCCwEhIASEQOEQSCS8ddfhlYS3cO9qrQuE6bJHH3000Os1FQczXYb5sjKaLkujc1XrRlflhYAQEAJCIBUCacaTRMKbKgdFEgJCIDME8M6G6bIHH3zQmYqDTJdlBr8yEgJCQAgIgRIjkEh4pdJwSImbVUWvKgJmuuyxxx5zqDhYMNNlK1asLI3pMqk0WOvpKASEgBAQAn1BIM14kkh4+5JxFZ6VSkMVWrG6dcB02UMPPeRmzpwZ6vViumz27FmuSqbLqtuCqpkQEAJCQAhkiUAi4a27Dq82rWX5Giqv3iCAdzbUG5YuXdbgna0spsvS6Fz1Bhc9IwSEgBAQAvVCIM148j+KAgnmlT7//HP30Ucfub/97W8OG7jYIh00aJBjGTeP8I9//CO3vPOoL3nSDtTbwkEHHZSIQSvrAOicKnQXAb6NK6643A0derRbsGCBw3oDAdNlO3bsCDa5TZgwwX3nO9/pbkEKmjqe6cBk9+4P3d69n7gPPvggKOnAgQMDyfjw4cPd6aef7o466qhSYER9vv766xBt+smkttX3GcKkEyGQKQJ8p3x/UT5Tln4mU7AyzCyR8Gapw/vuu++6Z555xj3//PNu9+7dYfWHDh3qli5d6jDLlHVApQEJWl0CRPe//uu/gnbYuXNnWG3svcbhj1WA++67zyW9J6wQ3HnnnbXCMAQt4xMID220evXqoE1WrVoVOKkw02WzZs1yM2bMcEWbgKTRueorlHv37m2YCETTw5Md/cxVV10V/BX1m2cA3bJli0NX2/8+meRgwSMacExy9913Ry+Hv/V9hlDoRAh0FAH4DJ4yX3/99R58BrfxqKMVNUDS33jjDffJJ3uDIvbrd4g7++yze4zj9C8bN76dWA0EMGeddVbiZDzxwT7cYDxpJeVNJLx9yDf1o5CsX//6127hwoUO/cNoOOyww6KXMvttroXzki5nVVHa4OOPP3ZPPPFEMOGItgPWAOII7/vvv++MWMWVFSIxe/bsHh9KXFxd6wwCrIgwCRkxYoRbtmyZw2wZf6g4INnEfBlx6haSJmXggJUL82DHEe92RfrmIboMLgyga9euDdrTbz8mpHGE9y9/+Ytbs2ZNUDc/vp3zffI+KAgBIdAZBFrxGXI5cOBAZzLrUiqQ3Z/+9Kdhv4EJzKeffrrHOM7Yz7iSFCZOnBhYEkpafUp6rtvXEwlvFjq8r776qps7d26PTtwq3b9/fzdq1Cj7memxLhLeZhOOZoCzRAxBUCgWAkhxb7jhBjd69Gg3b968YCJJOz3yyCOBhBCpX9wEJo9aMBvPQsrr1w2iN2nSJIcqw+bNm92bb74ZvMdgtGLFCkdHPW7cOP+R3M4ZQB9++OFw8tJOQZDQ6PtsBzHFFQK9R8DIbjM+Q59cZIED0l0k0K36Deq6b9++3oPVpSdbSXfJNpHwdqlMYbIsiTNDQAJlgdnEhRde6H7wg9ODS19+eSDQrbP7OnYWAV7c1157LVa63ion9CEtQCL4mP2AdF4b/3xEsjtnVn3++ee7IUOGuMWLFwf6vOSOigNS93nzbnZTpvwklSTz008/db/73e+C77JIks++oHnttdcGpJa6TZs2LVATID36onfeeacwhJfJwPr16xv6yLT1TvN9pk1L8YSAEGiOAPslWKlO4jNwmUMO6efyXLVuXgMXqDKwKtQq0C/ZXgiLCwfwQ1FVwxIJb7NlQL9ivT1H39A22JAGZJcd5xBeG1ghZHmJxOug0mC6n0j/TjzxRDd58mS3bt26hnZJal///LAv2wAAIABJREFUhT/zzDODJVc20PihyB+3X86qnpvpMqSZ6MIzczfTZX/+8/tu+vTpTSUOLKcvWbIkUHWBPHda8pm1dDfaznTK48ePDwkv99lkkme/45cRicVpp50WlA/VoksuuSS1tFffp4+kzoVA9xCgv2CJ31cHhM9gK53VtCLwmVa1TyvdtXQwMGABe/AIEfzN7kcccURLfVp7vlNHxpNWUt5EwtupQsSlg3T3ueeea7iFpMUnu9zMi+ySd11UGiBFt912m7vsssvcCSecEGyI8SciDY3k/fAnRBCHIi/VeMWu3Sltg17qsceOcosX3x+qOLC6wuYn1B6SNlGgM8oyP1ILlv7Hjh2b6zfZjcbr169fQ7JIKvLsd/zCUA5ULCjTRRdd5EaOHBn0m74UyY/vn+v79NHQuRDoHgLsgWHDvR/YKMwqmx+K0q/4ZbLzV155JdD55zfCL4wHJKk2sBHY71+YjHdaGGLl6vQxFzu8bHjatm1bWBdmQ3TsNhMKb+R4YhLeHIuQSdYMonfccUci6YkrBLNBloMtIEFUKC4CfFfTpk11y5cvD74zKymmyy6//HL37LPP9vDOxk5jf4mOCSodeydDq9l4J/OKSwsJtj+5i1PNiXsuy2tMMtALZGNaWom4vs8sW0h51R0BJLu+dSkIIxPUsgTG8v/zf/5PQHDhYj/60Y+aFv2zzz5z+/fvD+LQZ2I6tgghzXiSi4T3T3/6U8PsAf3PMWPGFAGzhjJEl+gbblbkR29mnSxn+EsaNrtFijh+/DmS9hb03WAWzm5/LDmYhQ06awgV0l4zXUYHiO6vv0QHMXz77bcr0bYsvbHKhFqVr7PGZrYf/vCHhWo9fZ+Fag4VRgj0QADhnS8NhcsMHjy4R7yiXmCPBit4BEyJYaPcr0+03PhJ8Mf/rVu3BvtFMDBQVN1dq0Mi4fVF1ha5E0f0XdCT8wMgFUm6S9lQaagD4fXbIe05Mzz//YAY8WcSshtvvLGHekratBWvuwigeoKuPFIIiC/L4/yh4kDHhYoDNrGR/kbDCy+80NF2TSuxjJajL7/pyDHlRTDVAN7bK6+8MjDTVTRbxb2pq77P3qCmZ4RA7xDw9eVJAQtXZeEOvnSXfpAVP3/1PQ4RLDRZoD9lfwh/CC7R50U9Mg/iW0gdXgoVNWmRhQk0a6C0R1kYSEYKW4K2pOHH4uVHErhnzx6HbmRUh8mPq/P8EGByefXVVwemuSC6tBltB8nFuYG/POeXcsOGDcH9TulrsQSVB+k1omt1Y3MllmGKsjRn5ertUd9nb5HTc0KgPQRQi/KFPzyNhLQ3KzPt5dyZ2L/5zUuhdJfN56xwtSK8WJxgvLBg54wj/CH8QphSROFBLjq80RfEgCvSEQmvQjwCLF2wA/X//t//6/7zP/8zmNUxO7QAocDxAbqECsVEgA6ZCcmTTz7Z0H50VtaBRUtOu+Lpi1WaMgf01PizQL1mz57l7rnnngbddLtftqO+z7K1mMpbVgR8N99lqwNqXY8//ljQ3zN+s9EOkprU/1v92LMzc+bMYNxg75XflxIHdTk2wWUd0ujwJhLebha2iBLdbta3ammzXAFZ4u+aa64JNkMtXbosUGmwuqITBHlSKDYCbIZ66KGHgiX9NCVFX7vTm9fS5NupOHTsd921KCD6WCexiRqdPMtyqHOUndDr++zU26J0hEB1EcAKDxJZgr9/wfrEpJoz7uMQBws+bGZGaIKlBgv0pWyCQ/pdtJBIeLslhYWFI/L3Q7fy8vNo91wqDekRY4n8kksmBx+NPcVLv337+/ZTxwIjQPul/QaZxKxf/1pHapOHOgMFHzPm+4F9TKyTQHot8M5ik9rfkGH3yny075MlSwv6Pg0JHYVA7xFAIhoV4KGyWUSy59cS6W50nwaSWVZucXZjgb6QVT0s+fBMNNC3YGsYD54+UUYtAvNlWYY040ki4Y02YqcKzlJqdAcjXkp8M1edyqsv6UiloT30UNKPmidD10eh2AjQMWO5wbdW0KrEq1c/3RF1lTRLUK3K0pv75s+evgjzQX5Hjf4yZhOrFvg+cWLhB32fPho6FwK9Q2DEiBEND7KJrejqfFHTsJDff//3fw/+ILgWmBizzwNLPnGE1+KdfPLJPbytsnm2aCGR8HazoMcff3zDIMMmp02bNnUzy7bTloS3bch6PIArRYViI4BraTo0Ora0gdl70b7XtGWPxov7zn2PQdH4Zf7tbxaG5Fdlk16Z20RlLz8CCHr8STP9Y9Enzd2QQEc9qyL9zTKkEaAkmiXrZkHZVIEJC9MfYdPIr371q8CTUx7mLJLqysCXdaMllaXo19HrxC2xBToAlo4ViosAziUwQ9YO2aU2fK8QZfS2yv59xOmZ40a5aoHvEwscfkB/W0EICIG+IXD66ae7oUOHhv0o/eOjjz4amH4soqUCags59VWcfAR27doV7r9hHIer4aAq6pXSf4YVet+6Q//+/Xus5Pvxu3GOSkMr0ptIeNPq9PWm4EcddZQ799xzQ8JLGojUUaPAHqbvptY/701evX2mLq6F28WHmaENnNY2LHXwgdsEhjT5mKKqK+3mpfjdQ4A2mz9/ftixtZsTm9f4VvtCmtLoXLVbrrTxWXKE7GJNxCf8ZX9v2/k+o0uxabFTPCEgBL5FAD4zefLkhr4UFTH4DM58BgwYEEYmbhFMluEcg01ncWHJkiVhXSDG7HPA4yPknf4F3VyuG5lnLGHDL0TfAt7aDjroIPtZmGMi4e2WDi81p8EZLBk0fQkLG0bYOWgSFkDFG5IBmyVqLHVKwtsTccywQG75oJnV8p6gkuK/7JgpwcRJkaT1PWtS7yt8Z+ZdpzdIfLN5bX2fCC+z8axJr+mk8W5iV9h/b5Fm/PznP8+lv+lNG8Q9w/eJigpt2+r7zKNfjSuzrgmBMiMAn8HZgs9n6GeifAYpKRZxijAusjKXtDrnq2fQLvAwK/P27dvdnDlzgt+2Z4eVXV/YxfgPv0tKP8+2TiS83S4UkqGFCxcGytD+oMO5/cYbFLsE8+qYy+Itpdtt5adPeyDh5YP2JWMWh48FssvOTYXiInDhhRe673//+8H3hVthvB9yZAMpqzs4FqGt49rYavXiiy8GHVte36eVo92jvynDnrVJGriUOdBmLEnq+yxzK6rsZUPghBNOaMlnqFMZ9gc06/MRbsX1n9ZejP+sHCJBzjq0UmegPLkRXjK/9NJLAxNlDzzwQCCRiALNoMtOP1s6zxJAuRZORnvYsGEBIbKJCTEhDPjhxjUhZLeIs7vkGtXvDt+U/12Z7VkkrixZsUwFeTIyzIYndh9zzST6SBHfeOON0nrUo3NGCkrnzHs7YcKEQiw3tvM2xk3KW32fZaxnO5gorhDIGgGkvPAZ9FzNe2W0DAgS8uIz0bL09rfvVjiaBgLKefNudlOm/CSX8b+wOrw+UJAjNrGxRIpYnAGWwFI5AOalBxq3e9svdxXP+WgXLFjgrr322rB6tI0f0EFC9weJIMTnk0/2uiOPHBSoodBWtvThP6Pz4iNA2xM4Rskw19HdQu8VCQWdNqa9aP9jjjmm15XLQp0BSwRsiP3b3/7m/M6ad5alOmyC896WQUrNJBI7mWZWDeDpI/3A94mZOdoo+n2is1uGevr10bkQKBMCOGXgm3z77bfd1q1bAz7Dhi6+PdyX58Vn2sHQX51FKOALRi64YGLgYZUNaghAIPGoNhx77Cg3btzpfVJxa6eMvY2bKOHtpg5vtLA2wCJ5sEEQ8bQNwtH4Wfyuq4S31SYk2gRSy99JJ52URVMojwIgANkyqb3fAfalaFno8FLmKr2nreriT1haxe1L2+lZISAE4hEwPnPxxRcXhs/ElzT+KoSX1VoLPg9j3Dcvq6wKmlTVj2PPZX0svEpDFBBAKwJw0XLptxAQAkJACAgBISAE0iJQZj6ThoeVsX6Jjie6aZYs7QuTZ7w6qjTkibfyFgJCQAgIASEgBIRAbxAw7YBmzyYS3mYP1eGeXAvXoZVVxzwRSNNB5Vk+5S0EhIAQEALVQSCR8Gapw1tEOCXhLWKrqExVQiCNzlWV6qu6CAEhIASEQHcQSDOeJBLe7hSpXKmWwWZeuRBVaYWAEBACQkAICAEhkD0CiYS37jq8dbXSkP0rqBzrioBUGura8qq3EBACQqCzCKQZTxIJb2eLUr7UzLVw+UquEgsBISAEhIAQEAJCQAj4CCQS3rrr8ErC678mOhcCnUcgjc5V53NVikJACAgBIVA1BNKMJ4mEt2pgqD5CQAgIASEgBISAEBAC9UQgkfDWXYdXKg31/CBU6+wQSKNzlV1plJMQEAJCQAgUCQFcG6cNacaTRMKbNpOqxpNKQ1VbVvUSAkIgKwRk3jErpJWPEKgXAr3pWxIJb911eCXhrdfHo9pmj0AanavsS9WdHNuRVHSnBJ1JNY3L0c7kpFSEQPcQKPt7XPbyd6Jlo87B0own/yNNxlu3bnUrVqxME7XQcXbu3NlW+Q4++OC24ucZef/+/W7p0qVu4MCBeRajz3nTRp9//nmf01EC5UXg73//u3vyySfda6+9Vt5KOOe2bNkSlv+5555z1KsKYc+ePbHV6NfvkNjr/kUN1D4aOs8Dgc2bN1eCz1APC1WpE1wzbUAoCUf7n//zf6Z9xCUSXl+Hd+XKlW7NmjWpEy1qxHYGnLKpNPz1r391jzzySFGhV7mEQA8Emulc0edUKbz11luOvyqGfv36uUMO6RdUjXOCHaP1/e///m8n0htFRb+7icBBBx3UkPxLL73k3nzzzYZrZfzh85kq1snaJNq32PXokfGklZQ3kfBGE/PBjd6r4u/e6IdUEQfVSQhkgcDhhx/uRo4cWVlSmAWGWeeBmgbtFg1JK2NR4hF9Tr+FQFYIVJHPVK1OQ4YMCSfO/uoRk+k4qS5kt9VkOpHw+jq8J554ohs2bFhW72LX8tm2bZtDEpomlE3Cy+AzadIk57dbmnoWLQ4rC6wmVO3jLRrORSgPHZS576YDu/baa93w4cOdr3r06aefNhRV6i4NcOT6gwnKKaec0iDdjRuI/EIihWk1KPnxdS4EeoMA75itIH399ddu4sSJQb+yY8eOHsn5q9k9bupCLgjAY84999ywb7FCJE2muZ+mb0kkvJYBx6uuusrNnTvXv1TK89tvv90tWrSolGVvVeihQ4e6BQsWuOOOO65V1ELf37hxo9uwYYMIb6FbqTOFY1BC6seARBg1apSbPn26Y7L51VdfuQMHDgSE2M4PHPjSffnlgSAu9xTyQcBXV/h2ufEb/V0GJJPAaJUsn/ZRrj0RoG+59957U/UtPK3+pSeGWV2x/sXvW6xPaVaGVuoMPJtIeOs+61Fn3ezV0j0h0DkEfNIbTTVpRk9naOQ3+ox+dxcBG4jIxZYakwYk2s+X+qYZlLpbeqVeZwTSjuvqX/J5S5r1LdHJtN+3pFWXSiS8+VS3OLmWTaWhOMipJEKgPQRMwmtP+YOSSVp80kQ8pL2DBg0KjpAufitkh4BPdMmV9okOSHGlkTpDHCq61g0EbHJF/2L9h6lQ+fnZPf8a/YlPvvx7Ou8OAtaPW99CLjaRtr6lWc5p+pZEwlt2XdBmwKS55w+6aeIrjhAQAu0jkDQoJX1/DE6oONiyF0dI8ZFHQn6/UXOwa3akVHbe7rGdZ+Pixl1LW4Zmz8bdi7vWl7yi6fE7Gozocp28/BCVwFhb+3F0LgS6hYCvx+vn0apvIa69y/Qprb4hix8XN+5e3LVWedj9Zs/G3Yu7Zmm1e2yWVty9uGuWZ/Qevwnct2ATESO79jup/dJYgEkkvJZpnY/MBg3kOuOguguBbiNgag32vZkkxn43y9/i2NHi8tuuQZJ9G9V2L+k6abTzbFzcuGuWLveS8o5e95+xc44WSKu3eZGGPRuXnn/NzjkyCFmwQcrSYUDy71s8HYVA1gjYJCtOymvvq18me2+j/Y8fl3P7bUf/m+Wa/5v0467Zdf9e3HOWhx39OP6zVo+4a+3kZenb0Z7lGL3m/7Z40Wt23cpvR7vO0Q/WBnbN+hd+G9kljqXD2EE790nCW3cdXlQa4kzuWCPoKASEQN8RSJLCRDs9Pye/A4yTqnC/2fUjjjgi9n6nrkfz70261LdZHSyPaNrR30nx7Hr0mPS8H89vC85t4PEHI/96OwNSNG39FgKdRMDe1WiaXIeoWfDfd/87TPo+otf5TWj2bPSZaJ5J96PXu5lXqzIl5R2tezSdZr+tDTj67eX3L3a93b4lUcIrlYZDgh3iBqzfCDoXAkKgcwjYYBOV8pIDkhbr6OJytO+zakfqWtQ6RdvBbx9f8mLxTMpmv3UUAlkhYBNq61vI174rv29BwDVgwIDAioOVzeL19ujn1ds00j6XZV7RMsXl7Ut5o/H5Hb3v/zb8OUb7Fj8vztvtWxIJr59pHc8l4a1jq6vOeSFAJ4gOFsE2sVlHmVeZlG86BHxpvN9mkAwLaZYbLa6OQqCTCBjBiiO90Xx8ghW9p9/pEWiFY/R+9Dc50a8wKbH+JalvSV+qJmbJ2klEcYWAEBACnULAiJKvd9eptJVOZxEwMuEPRuRgbZhWt66zpVJqQqARAXtP7b1svNv5Xz5R63zqjSlmmVdjzt+swBkhjd7rxO/DDjusRzJ+G7Y7kf6XHqn980LddXiZcfAiKQgBIZANAnRePnHyOzZKwL24P7un47fLtYZjN45+G/iYc06b8QfRtby5riAE8kYg+j76/Yv/Tlu8vhwhapYm9e5LWq2ezzKvaFks7+j13v42zOxIOhaifUu76gykk6jSUHcdXqk02GumoxDIFgE6O1NviOZs6g7+deJHBy/uc92O7dw/9NBDA7UK/3lLh2Mn77dTrrjytPN8p8odgOpJce23Hf2BqF0JjKWhoxDoBgL2DdG/4IrW/37oW/zf5E98/1rab8gvu+UZTcu/Xpa8omW2eqbFpRUGlp5hHm2TvvYtiYTXMq7zEQmvNXCdcVDdhUDWCESJEr/Z2GAdIR0fA5Yf7J5/zT9v5347cf087Lyd59uJa+n7x3aebyeun4edR5+3duAYbTN7RkchUDQE7F21o9+3UNboe+7/9s/j6tXJ+51Mq1VZi5CX9SeUlXM7WjvF1aGdayK8CWih0tBN3ZSEbHVZCAgBDwG/o4tOPv173iM6zRgBtUPGgCu7jiDgv7fRvqUjGSiRXiHgt4t/3qvEIg9JhzcCiP1EpUFBCAgBISAEhIAQEAJCoPwIJEp4s9DhZRlh+/btgXFmgxKDxMcdd1zuqgRxZjKsjDr2RCCNW7+eT+mKEBACQkAICAEhIAS6j0Ai4e1+1s799a9/dfPnz3fbtm0Ls+vfv7979tlnA9IbXszppG46vB9++KFbu3ate++990LEL730UnfOOeeEv/2TL774wr3yyivuhRdecJ9++mngme5f/3WCu+CCifJS5wOlcyEgBISAEBACQiBXBBIJbxZmyf7yl7+4N9980/39739vAGHPnj25E946WWmArD711FPBn092aZQTTzwxlvDyzB133OFWrVrV0H4rV650U6dOdffee687+uijG9pVP4SAEBACQkAICAEhkAcCiTq8WRRm586dDWTJ8vzDH/5gp7kd62KHF2n6pEmT3E033dQg2W0GPOoLS5YscY888khs+0F6n3jiiQbf5M3S0z0hIASEgBAQAkJACHQTgUTC220dXvR3fWnid7/73bCeW7duDc/zOkHCW3UrDRDX1157zb311lttwbxp0ya3YsWK8Jnvfe97gVSXowXub9myxX7qKASEgBAQAkJACAiB3BBIJLzdLhH6n0aIILujR492Rnp37NjhuK/QXQQw+fGDH5weZAJZnTlzpjvjjDOaZgpJRg0F/WsLs2bNcsuXL3ccLXD/pZdesp86CgEhIASEQM4IIGhCHY39GvwpCIE6IZBIeLutw8tHZ5vVcE83ZsyYEHfy/uCDD8LfeZzURaVhzJjvB0T36aefdg8++KAbOXJkU7g///xz99xzz4VxIMoXXXRRYFXj1FNPDSctRNi8eXOix6wwAZ0IASEgBIRAVxBAQAGxZSVvxYqVbu7cuW7atGnuhz/8oXv77be7kqcSFQJFRSBx01q3C/zuu++GWQwZMiSQNLIBirB79+7AXNlJJ50Uxsn6pC6b1k444YRAHxdpL51jq4DlCjYVWqDtDj/88ODnEUcc4YYOHRrq9UKOP/74Y21eM7B07DMC9o767yvnSK7MeDxxovftGgWwuK3S6uv9Kufl49nnRlUCHUGA95qVUYRJbAhHoIR6ICum9Nn+qhybkfMcXztSYSUiBNpEIJHwdluHlw/RrDMg4R037nTHkWv8saEtz2ASXhtE8yxLN/OGGLQTkBZAZC0gETaMBgwY4Pz3hk6W+LLWYGjp2C4CRqwYzM19LecWfPfCFse/5p/bfZ61c/++f96p+1XNy8fK2sKO7fYp9pyO7SHAt4FA4f3333e7d3/ofv/7twM1QVs5tfE1LlVWVAcPHhx3S9eEQGURSCS83awxs1CWuy2MGDEi+PiQFtosFJUGBh0jUxY3y2PVN631Bkuf7Nrz5oMbvJi0+AGJsIIQaBcBk65CrIxccbT36euvvw6TjDuPu8YDcdfjrvlx+3q/k2m1KkvWeZGfH5iU5N1v++Wp4jmro88880wwhtIfR6W3rerMXhn2ahx66KGtouq+EKgUAomEt5s6vB999JHbtWtXCCSEF9KEtNAsBkB4IcZ5Ed46WGkIG6CNkyRTcnFJ0Bnv27cv7pauCYFEBKLSVZ/kJT6kG5kjEG0X+nCbnFgbStrb+WZhTFy3bl04VrabA0KJ8ePHt/uY4guB0iOQSHi7WTNmpOjpEsxCA5KB4cOHh9myLIMukumHhjcyOpFr4YyAVjZC4J8IMMFlovu3v/0tEROIlEIxEGBFjmVxE0r4BNhWfYpR0mqV4rjjjnN33323mz17doNpz7S1xCKSb0Iy7XOKJwTKjkAi4fV1MTtdSfSNfP2iY445Jthkcuyxo8Ks8pYOSsIbNkWfTvr169en5/VwPRCAyN5///2Bfec4tZl6oFCuWrJBdeHChe7ss88OCx4lv0h87VoYSSd9RgB374sXL3bXXHNNqAaYNlGzqpM2vuIJgaogkEh4u1VBBjaU6y2wvLJ+/XqHXtKf//y+XQ4I8TvvvOPGjRsXXtNJ/ggghUcq709YkkpF20Z1epPi6nq9EUC6yzKt6fDXG41y1J4+gM3Hp5/+jS1vv9SQXCS+SHrp80V6fXQ6cz5hwgR3112L3O2335b6u0Gye+aZZ3amAEpFCJQMgUTC2y0dXgY2czgBVgxw8+fPD2CLkijfE1vWuEqlIR7xOALLwMaAxoYiXzrXv39/N3DgwPiEdFUI/BMBNqjZZjQDBbNJ3Vxlsny6efQ3E0E0UAGoQkDdzO+rWQ0jmGUbNq8ayTU1B7OwUYX6F6UO6EdfccXl7ssvDwRjqN8mSWVEnYE9MwpCoI4IJBLeboHBwMZA4IekDzVPSw1SafBb6NtzTIxBeq3NsPHI4MaO388++8z5EyUIi0zffIudztIhwAoCS+VxksN0KRQj1i233OJWrlwZFAZj/9dff30PYl+MkrZXiilTpoQbptiUyvcfDT7p5Z5tZtMmtihSffsNnpdddpl78cUXU3m2ZLOarA/1DXM9XV4EEglvt6QreHfxpYBIcoYNGxYiiNtaI1NYckACjJJ+1kES3njEIbtICWzpmcmLbS70NyPyNLYeZfomHkdd/QYBpLtGhnxMWBkou/3maB+a1wZcH9e+npu5OD+dAwcOhD9Z6TGJr0+skPQi5VXoLAL0vUuWLAncvbdKmVUGvGFq0tEKKd2vKgKJroW7VWHf4QR5zJt3s/vNb34T/OGydtKkSWHW+/fv7yENDm9mcBJdZs0gy8JnwSDmm7SB+C5dujTwjLd27dpwsoKUjo0VCkJACFQbAfpJ/iC+Ju2F9HLNfoNA3MSm2sh0t3aQ3TvuuMMtWrQo7HebWV9AUDF27NjuFkqpC4ECI5BIeP2l6U6VH+mA70GNj3Po0KPD5JEO+KbJIFN/+tOfwvtZnkilIR5tpAPnnXeeQzJv4ZFHHgmucbTAxgh/97Zd11EI+AgYCTKpoH9P58VHgHHC1BqipLf4pS9vCbdv3+7mzJnj/D534sSJ7umnn3YzZ86Mrdhpp52mFbdYZHSxLggkEt5uAIAbRHQ+LbCJI7qRA9NkSAct4KQibhnN7nfraBswupV+mdPFBzudqt9OpuJAvZjIzJo1KzcbymXGto5lt41Ndax7Veps0l2R3u63qJFd0w8nx6lTp7qHHnoosGrEJnB++0Erbj4aOq8rAolKVVH9s04AhM9vf8Ma+qDRJZjjjvvWFi954oIYnd+s9d+QOGWdZycw7kQaPpE95JB4O7o/+9nPgqyQMJg1DZ5j2ezGG290mMxREAKtEBDZbYVQ8e8fOPCNlYaozW1fam9myVBxsPPi16x4Jdy4caObO3duuGmQPhc1wHvvvTfUeUf3nd+oPLz00ktBJeiXTz755OJVSCUSAhkikEh4u1EGpLlI/izwEUY7v1GjRrn77rsv0Acj3qBBR/aIY8/r2FkEUFe49tpr3b/+6wTXr98hQeJIc+MC7YbRc/R5N2582+3d+0mgjkL8PDYZxpVR14qNACs32GkV6S12OzUrHaQKs1hMjG3zmr9ZrdmzutceAnFkF29rWP+ICmcgvYyjCIveeustbSBuD2rFrigCiYS3Gzq8ECFmp80CHy5EKu9gKg1RQp53ubqdP44+2nH2QZuK4Ha7Vaqbvshu9doWtYaotJda0tYiw+23NxPDV1991c2bN69hNQ2ye/PNNyfq5SJ8eOyxxwIbvWwgrttY1j7SeqLqCCQS3m6oNJQJzDqrNJSpnVTWciMgCW+524/Sm2RuZ3KYAAAgAElEQVTXVoXsWhzBkkms9tobsvvrX/86EBTZPgnUGJDeolYWh7GfA6QX3d6oBNiPo3MhUBcEEglvXQBIqmddJbxJeOi6EOg0AmahodPpKr18EDBd3mYkDAIn0puufdB3fvLJJwNya2SXPS+4E54y5Sctya7lohU4Q0LHuiMgwtvkDdDyWxNwdEsI9BEBHBGI9PYRRD1eSQS++OIL9/jjj/ewsfvggw+6iy++WJOGSra6KtVtBBIJbzd0eLtdmU6mj0qDCG8nEVVaQkAI1A0B9aPttzhk9/777w8c+pjXUeye4+DnrLPOEtltH1I9IQQCBBIJb911eOVaWF+IEBACQkAIZImAeU9btWpV6D3tjDPOcEh229lMnGWZlZcQKAsCiYS3LBXoZjnZbdxMH62beSttIVB1BKTOUPUWVv3aQQCyi/c036EE3tPYoJZkHrKd9BVXCNQdARHehDdAVhoSgNFlIdAhBIqgw8vGoL179wY1GjRokCa4HWpbJdMeAuY9zRxF8DRkFwsL2nTWHpaKLQSSEEgkvHXX4ZWVhqRXRteFQLkRgORijP/ZZ591W7ZscdbXDRs2zP385z93l156aWEr+OGHH7rPPvssLN/gwYObmpxCaoiHSxwQ4PinVfwwYZ1khkAc2cU1sO89LbPCKCMhUGEEEglv3XV4JeGt8FuvqtUWAQgjS8S+jqSBgYtsPAcWMUCKXnzxRffUU0+53bt3h0VcunSZmzZtavjbTsxZwbJly9ybb74Z6IOa6+/bbrvNnX/++RZVxxwRiPOeduWVVwbOIvCWpiAEhEDnEEgkvJ3LQikJASEgBHoikLUOL2T3lltuadCR9EsFIRw+fLh/Kfdzyrx27dqgzEil04YNGzYEHivNfivPseOfNPBkiQcukd60aHY+HhMS2mjBggVBm5AD718r72mdL4lSFAL1QSCR8NoyX32gaKypVBoa8dAvIdBpBLLU4UWN4YknnuhBdtkBP2bMmLBqRdKXhBRR5kWLFoXlS3MCSYZI+WQXMmUmrrhOmmPHjm2qDpEmL8XpHQJx3tOQvM+YMSPRVXDvctJTQkAIGAKJhNci1PVYRpUGBnUGyTIHLGMoCIFOI4Cu7ooVKxqSRU8SYmgkF/unuDouasDLFrq4RlyTyrl+/Wtu27Zt4W3qiRvaBx54wNmmKO7/7ne/K7S+cliBCp3QRyOxnzt3bjghYTKS1lVwhaBQVYRA5ggkEl5fh3fr1q3BEljmpetwhps3b06dYtkkvOj1LV68uHBLsqkB/2fEnTt3BoN6u88pfvkQyFKlAaLnSzyR7EY3BR166KGFAhEXvJQTonvhhRc6dDsfffTRHlJqv9CQ9t/+9tWQFEOmrr322sCG6759+0LCC2l+4YUXRHh98Lp8DtnFVfD8+fPD9qFtsbFL+8oEZpcbQMnXHoFEwusjg11A3zagf6/K52XytMYAVsc2qvL7p7p1BgEsFaxbt64hMaSeRx11VMO1Iv5A7eDpp58O1C5QAWkVkAAjzbYwdOhQN2rUqOAnVhogWEb8d+zY4SDIRSP6VvYqHcE5yVVwka2CVKkNVBch8C9JENRdhxeVBgUhIAS6h0AaAteJ3DHLtWfPnjApSN/3v//9UrhoPfzwwwPpbFrpHybLfCsOmFqzZwcOHOj69+8f4kAf/8EHH4S/ddIdBCC7uApGb9rUUZDcI+0V2e0O5kpVCMQh0Fpk4FywrMbGjjKTYFQ0kHyk3elcNtfCtuw5YsSIuHYuzTUG4DiTUaWpgApaOAQgu0g+LSDprKrJJ5/YU18Is+klc+6rqu3fv98dOHDAYNGxCwjEmcGD7MpVcBfAVpJCoAUCiYTX7xgvueQSd91117VIqvi377nnntSEFwlvmVQakNxg0sY24BS/NeJLiF1KNnWYJCQ+lq5WAYGsdHgPHPiy4X067LDDSvVtt9PWn3yyt6Gufj9OOtTdApMAfyJg13XsDAJxZvDkPa0z2CoVIdAbBBIJbzQxWxaLXtdvISAEhECREfjyy/pIMdutqyS83Xlz47yniex2B2ulKgTSIiAd3gSkyqbSkFANXRYChUUgKx3ewgKgglUSgXfffdfNmTMntIhBJdkkyaa1sq/AVbLBVKnaIJBIeGuDQEJFtWktARhdFgIlQ+CQQ/oFXqys2CzjV9XeM3VNG1BvYCObQucQQCULT3Zm7xizcDNnznQPPfRQZfXGO4eeUhIC3UUgkfBGdb+6W4zipS4Jb/HaRCWqFgJZ6fAeeeSgBuDY2IU1gyqGoUOPbiD30Y3Gvs4uev9HHHFEFWHIvE44/Hnttdfc1VdfHe4TgeyyrwKnEmwYVBACQiBfBBIJb77FKkbuVZUCFQNdlUIIZIPAMccc47BHawE7tFu2vGM/K3WMElhsEH/99ddBHTn3CTBCjQEDBlSq/nlVBlfBeLN77733giJAdnEVfPPNN8vOcV6NonyFQASBRMLrd4yRZ2rxs2xWGmrRKKpkpRDISocXk33Yo/XD6tVPO3bRVy0MHjzYjR49OqzWrl27HB6+CHhawxSZhZEjR5bC+YaVt4hHsF2xYmWDq2DeN6S6N9xwg8huERtNZaotAomEt7aI/LPi5lq47jio/kKg7AhgYWbKlJ82VAMdS0gJpBfSYn8sTZc54DUNm+kWcEKxadMmR73eeeed0Msa90855ZRSON+wuhTtyDuD84jZs2eFuEJ2sbGLagOuoRWEgBAoDgKJZsnqrsOLhFd6V8V5UVWS6iGQlQ4vyF1wwcTAgY7veOaRRx5xr7/+ekAQrb8ruy1ryP0555wTOm/BnvW8efPc5MmT3fPPPx++RBCziy66KPytk/YQMO9pS5cuDe0en3jiiW7x4sVuwoQJIrvtwanYQiATBBIJbya5KxMhIARqiwAqDVmRXiav6FSygx4dXgvoXPp6l7h6LbvpqLPPPttNmjTJrVy5MqimX0er97Rp0xykV6F9BOLIrryntY+jnhACWSOQqNJQdx1eqTRk/SoqPyHQXQSQvD322GOBpDcuJ6Sh6LmWKcQ5moDcL1iwwOHoIC5gE/b66693ciYUh07za6jAzJ8/3y1atCiU7IrsNsdMd4VAURBIlPDaEl9RCpp1OaTSkDXiyk8IdBcBdCrPP/98h9WG9evXB+ajtmzZEmRKf8cmriFDhnS3EH1MHb1brC1YGD58uJ02HJFSY/v1tNNOc+vWrQusM7BxDzWGyy67TOpaDWil+yFXwelwUiwhUFQEEglvUQucVblMwispSFaIK5+6IZCVOkMUV8ggf1deeaXz7dLiiIFNX0UNEPYZM2a4q666Kixis/JSxzvuuCOQ5mJi8eCDDxbRDZFr7wTvaUh2zaEETyMpv/fee+VQoj0oFVsI5IaACG8T6BkgFISAEOgOAlnq8MbVALLYjDDGPZP3tXbLDEnW5tu+tRre0+bOnRs6lCA1vKfdeeedwrZv0OppIZApAomEt1s6vJhy2bt3b+Dp6MCBAw2V7devX2AI/eijj264nsePOtvhZVOGBUh/knkd2tKM2lt8/1g2MuGXXedCQAgIAbynYbnD39jIb3SgNZHQ+yEEyoVAIuHtlg7v9u3bg53SuPdkOZGNIhbYNYwO3bnnnhssN7Ikl1eoo2th2mbVqlVu586dIex4D8LMUTQQF5M8SRMj3h9JQKKo6bcQEAJlQeDll18OTLpFya68p5WlBVVOIdCIQCLhbYzWuV9Idbdt29ZAdC11zAXxh61MNlqwo/qkk06y25kf0Xurgw4vmzGeeOKJwE6nde4GNjuQ4wgvExbsmCYF8yMvKUgSQrqelw6vkBcCzRDASQeuglFjMBN29Gc4KkHvWytXzdDTPSFQXAQSzZJlVWQ6EkgVf5xbgPRixNtfXrd7WRzroNJAx/7ss8+6KVOmBGZ2omS3Gc67d1fPLWuz+upe5xHIyrVw50uuFKuKAGpay5cvbyC7rDwuXbrMsdolslvVlle96oBAooQ3aam606AMHTo0kOQOGDDArV+PvtSsUPq7YcMG98EHH+Qi5a2LlYatW7c2bMZI2757934SRmWiQjv6AZWGOqqF+BjoXAgIgfIggHDl8ccfb7Cxa97TWOWqw2pfeVpLJRUC7SOQSHi7pcMbLSL5DB48ONgAcMklk93q1U+Hpl/Q8UVXNA+1hjrY4WUz2rHHjgqahI4d96OokvjuV6PtZb+ZiFg488wz3YoVK3pIPzRAGEI6CgEhUGQE4rynGdnFdrOCEBAC5UcgkfDmUTWWi0aMGJFH1rXNc9y40wOXqxijZ5MgG9bSEF5/BQA9XWyYJllzqC24qnhTBKTD2xQe3cwIARx5LFmyJJDsWpbynmZI6CgEqoNAoQgvs2xfcgiJystSQ11UGsD3rrvuCt5odHrTBNrJ9/aElF5kNw1yiuMjkLcdXr8sOq8nAkne09iglsfKYj1bQbUWAtkgkLhpzZfgdbMo5IM1BEgUJrHefPPNMLsLL7wwN8Jbh01rIdBtnqBq4nuoev31193tt98ebIBjAFEQAkJACBQdAdTlbrnlFrdy5cqwqBMnTgxcMovshpDoRAhUBoFECW9WOrz79+8PzL0g2YXsml1eOh4MfOelB1oXCW9v3uTPPvuswf4u1h3MwgNLgTfeeKNjspJX2/WmTnpGCAiB+iCQ5Cp4wYIFuQlZ6oO+aioE8kEgkfBmVRzsHEbtuUJ2WVLKS53B6i7XwoZE49E85GGdwSYoFgP9X2z04jVPmz0MFR3jEKiTDq9vcjEOi7Jcq4LqklwFl+VtUzmFQGcRSCS8Wak0xFUHSe/8+fODzVTjxo2Li9L1a1JpSIYY18/YSIb44iYakrtmzZqQ/DKJWbZsmRs7dqzcbybDWPs7zXR4//SnP7myTzj9PpQVLIhWFYJfr7LVR66Cy9ZiKq8Q6BwCiYS3c1k0Twmj3rNmfWN79/nnnw+WxpEavvTSS4Ge6JNPPpmLpFc2ZJPbDcLLnwUMsv/rv05osKHMpOX9998X4TWQdEyNAN8/KzxlD76eO/sT1q5dW/YqBeU372Nlqox5T1u4cGGofoXUHbU5uQouU0uqrEKg9wgkEt6sdHiHDBnirrrqqsCsFaaxrrnmmtAsFpJDBopbb701c31QSXjTv1To6k6Z8hP329++Gm4AgbRs3/6+y0tCn770ipkXAs1UGspIqprhyPfAn0L2CBjZ9V0FI2i5665F7oorLpeFmeybRDkKgVwQSCS8WZcG3TB2xk6dOjUkvJRh8+bNgQUHbYDKukXay4/l6eHDhzc89OWXBxp+64cQSIsA0jfMEpY5IOE1kluF+lhblGkygqtgVglZMbByG9llkp6kk4zVIJ5l3PHdCUOesSpEQOXGf574X3/9tTvooINCAQ3xmdhx3eITj/SjaZOmmXskT4159sbpKAQ6g0Ai4c1LT2vQoEGOwcEGil27dgWdAM4NsgxSaeg72rSlghBIQiBJh5fv/xe/+IU7++yzkx4txfU5c+aEKx4snV9//fWlKHerQk6aNKlBKNEqfl73IZVJroInTJjQQFatjDzzyiuvuBdeeMHt2LEjmHSNHz8+WIVkDOLa0qVLAys11157bcMKFiorPMfE39QkmPTg1AKHPsSHLEPASWfkyJHuxz/+cWDRhnyfeuopt379+kCVb8yYMYG6Rd4btw0XHYVAFRBIJLx5VY5NUH5AtSIP8imVBr8VWp9//PHHgVtiiwlpUWdtaOjYLgIDBw4svf53VC0s60l7u5iniY/EsgwBAnn//fcH5NSEJ5hMvPvuu91ZZ50VS3aRvPoEmT6MgNBlypQpwfm+ffsCXWykxZBVP2zdujWY4OCSmMmNSYZZpWRPCu8DNst3794dPIbK3oYNG9yBA1+6P//5/aCs3KC83EPodO+99zbsl/Dz07kQEALtIZBIeKOddXvJ9i42TgtefPHFULpLKsx081jazINk9w61bJ9iwNu0aVMgqRg1alSwTMfmNHSt6aQtnHnmmXITbWDoGItAMx3e2Ad0UQikQIBxBBUG39wlZPexxx5r6j1ty5YtoXvh//zP/wwkr6SFmcV2Vqv8sdNXS6CPpF/Ews0nn+x1ixffH2ygu/3224Ja3Xbbbe7UU08NHPgQF4cYkGp/g3CK6iuKEBACCQgkEt6E+B2/TGfyzDPPBCaunnvuuQbSxAwbBwY2U+545i0SZPnJ77BaRK/FbTB59NFHA8kEGw4JtKHpx/EbHbmf//znubVbLRpClRQCQqAHAhDUOO9pEOBW3tPM8RF24LE8Q9/fSbI5dOjQgIhbOfbu/cTddNNNQd85c+ZMd8MNNwSSZwQJkG8ECEiNL7744liJdI/K64IQEAJNEUgkvFnp8EKUsLlry05WWsguem/nnHOOXcr0iEpDFZYgOw0aOml0xrSbT3ItHzMzRyetIASaIZCkw9vsGd0TAkkI4CoYlYWoq2DUFNIQV38MYpNZp4UdrFaOGDEiLP7o0aPD/SqXXnppSGopK/q9EF50f1kJ8TfHhQnoRAgIgbYQSCS8baXSx8h+RwPRpSPAWsOVV17Z8U4nbVFRaairhNd018DqkEP69YBs2LBhoR4abWfxWa7DpjKTFHXQPWDTBSEgBLqEAK6CfZOWZIPUFGFKGrJLfNQeCEh6USlg/Onk6iKqDlhwiAtRJyumFmFWG+Ke0TUhIATaQyCR8NoH115yrWOzGQXJLcr/eB8iIEklP2a/kN0ieOiqo4QXkjpv3rxgOc9akuU1Pxx11FFuxYoV7qOPPgpUGdBF69fvkGC5cPDgwR0dIPx8dV49BKTDW702zaNGeE9bsGBBqA7HBByyeuedd7a1Sse4g6AFCTFEmT5u+vTpqQlzHnVXnkJACKRHIJHwpk+ivZjs3L/rrrvae0ixM0PA9MuSMoQUM0Hhr1XcpDR0XQgIASHQCQSSXAWbWbB28qBPwyoCAdK7aNGiwA58Gv3fdvJRXCEgBPJB4F+Sss1Khzcp/7yvm0pD3uVQ/kKgqgigw6sgBHqDANZiVqxYGaxGvffee0ESSHaxdNAbsmtlQP0B0ks6pIc5MawqYOZMQQgIgXIjkEh4y12tvpdednj7jqFSEAJCQAh0GgHI7i9/+SuHOS/bOAs5Xbp0mbvuuuv6rFYF6cWdPZJd0l2zZk3gjKLT9VB6QkAIZItAIuHtlg5vttXrfW6S8PYeOz0pBNIgIB3eNCgpjo8AziH+4z/+w82ePSsku1iGgexeccXlHdvkjIUG9IDZiMvGXMyDEfr16+f69+/vF0nnQkAIlASBRMJbkvJ3tZjRnbNdzUyJC4GaISCVhpo1eB+ri1rBww8/HOjWmmUfvJrhqnfatKkdtwyDRQUzI8Yma8KAAQPCWhgJ5gJEHBNiCkJACBQXgUTCW3cdXlQaFISAEBACQiB/BDDPheUENpIZ2cWM2NKlSztmq508cFxhAXUJbI6j1vCDH5weXMbjGmYZCevWrXPY/oWIr127NnDGY8/qKASEQPEQSNw1IpWGQ4rXWiqREBACQqBmCPTWVXC7MOHxE+sM5557bkByzfMnntfGj//GARKqDhdddFGwmQ3HEHhkO+yww9y2bdtCVYdOCIs6kUa79Vd8IVB1BBIJb9Ur3qp+2rTWCiHdFwJ9QyBvHV4kc3gO/Oyzz4KKsFyNBK/THrb6hlK9n45zFYytXOzuYuKyU4GNcAcOHAhsiyM1JkBkyQvb5L7zissuu8xhGQKpLqQXCfCkSZPcj3/8Y7dw4cLAprxfLsydoWeMDXo/8J7hbph80A32w/Dhw4NneFZBCAiBziAgwtsZHJWKEBACbSKQl2thiO4rr7ziXnjhBbdjx46A5FD0IUOGuBtvvNHhFruIngIhZZT3L3/5S4g0Oqw+GbMb1HHTpk2Bbqld84+QLVQCikzu8Z6GGgOmwSxAQDEbFldni9ObI+2NhYfzzjsveB8gv7wP4Bv1tgYJxYID7oB37/7QDR16dOiljWcI9gz7QMyZD/d8vfWTTz7ZLV++PPDoabrCVnY2zOGx8ogjjmh4xu7rKASEQPsIJBLeui+pYKVBQQgIgWohYBJDTE2ZLqjVEJ1NCFURyS7kjyX3559/PnTrTbmxTsCGrWhAAslyOxLsuIBU8Y033ug4cYzLqzfXOuEquN18If8400njUAdCCyGNhnHjxjVc4l1KSg/inCTBRXrdSQl2Q6H0QwjUFIFEwlt3HV6pNNT0i1C1K4sAZHfGjBkNEkO/sixNs5RcpECZV69e7Z566qlgGT1ati+/PBC9FPzes2dPaLYrNkKBLyLJxtkD6gIE2qU3roILXEUVTQgIgRwQSCS8OZRFWQoBIVAjBLLU4cVs1BNPPNGD7LKszyYlC7Ykbb/zPEL8KDOWCZLCIYc06n5avL1799pp7BEJb5EDFhMIkF28njFRMTWBIpdbZRMCQqC4CIjwJrSNOZ4oso5btOgM6gySZQ7/+Mc/ylx8lb0NBLLU4cW8lG1GsiLOnDnT3XnnncGmIZae+XaKpM5AWSB8Ftj4RDDvYnY97vjRRx+Fl9FDveqqqxo2RkGUi0p6qTck97TTTnPHHjvKTZnyk0LrGodA60QICIFCI5BIeH0dXgxsP/vss4WuSJrCtWMYHJWGJP2qNHllHWf37t3BBo8ylTkOIyQ7SXqHcfF1TQikQWDVqlUNOrtIdtkQ5X8vRSK7VqdTTz012Dg1efJkh3UAypyG8Pp93ZgxY9zcuXMtyVIc0YWN6sOWouAqpBAQAoVFIJHw+jq82Cbkr06hbBJeNuD4u5nr1FaqqxBohgCTKCS8fpgx45rCbtjyyzl27NhA2DBy5MhA+uwTdD+ef46k2lQCuO735X48nQsBISAE6oRAoqe1OoGQVFe5Fk5CRteFQN8RyEqH9/333w9Nj1FqVAOOO25U3yuQQQrorbJbH+lzWnUlVkiiqyR4BPNJcAZFVxZCQAgIgUIhkCjh9UuJDhjLYmUPSHkw15MmlM1KA4P4tGnT3ODBg9NUr7BxaJ/o8nNhC6uC9QmBrHR4sVjgE0A2pnXajmufgOjwwxBbXyXtkUceCZwkjB492o0fPz7Q500jKe5wsZScEBACQiBXBBIJr99hsuGBXbJlD/fff39qwls2O7z9+/cPTPeU3Xbjxo0bg8E5aiO17O+eyp8fAlgs8N8nNmtVefVm3759bv/+/Q2Ao/fLH2pP7MnohvOGhgz1QwgIASFQMAQSCW9U76tuJmHKJuEt2Hul4ggBIZATAripPeusswIVBiTbUZu87MfA3vCtt94q6wc5tZGyFQJCIHsEpMObPebKUQgIAedcVjq8dQObVZ5HH300cFiB04onn3zSTZw4sQEGPLa1stXb8IB+CAEhIARKjkCihNdXaSh5HXtV/LKpNPSqknpICOSIQFY6vIMGDQrs2ZpaA1LPKtt7xna42Q9nZQ59ZfSWd+3aFap0YcYQffkq6zLn+GorayEgBAqIgCS8CY2CSoOCEBAC5UeAJX4/sMSPy946BTa1/uhHPwqrDPn3bfWGN3QiBISAEKgoAomEN6rDW9H6J1ZLEt5EaHRDCHQEgaxUGo455hg3dOjQsMxs3nrnnXfC33U4QZo+cODAhqomuSVuiKQfQkAICIGKIJBIeCtSvz5Vo8rLnn0CRg8LgRIhgHQzalaRjVt1kvLSl/kSXVwWH3nkoBK1oooqBISAEOgbAomEt+46vLLS0LcXS08LgVYIIHXMIqDP+uMf/zjQ47X83nrrLXfLLbe4d999N3To8MUXX7ivvvrKopTyiHMK6uE7qeD8lVdecWvWrAnrhMQb++oKQkAICIG6IJDNiFNCNMvmWriEEKvIQiAzBM4++2x35plnNrjfRsqLM5phw4Y5c8SwYMGCwLNZZgXrcEZIcrE3jjT3lFNOcegvszlt7dq1DbaIJ0+e7NjMpyAEhIAQqAsCiYS37jq8SHhtEKzLy6B6CoEsEchKh5c68S3fdtttDZYKuA4Z5I/AMv/ll19easKLhHrdunUOCTaEPi5gomz69OmhJYe4OLomBISAEKgaAokqDVWrqOojBIRAvREYN26cW7p0qTvjjDMa1BsMFSwXHDhwwH6W4vjll43lxa0wVijiAoR+6tSp7r777pM5sjiAdE0ICIFKI5Ao4a27Dq9UGir93qtyBUAgKzu8flXPOeecwCHD+vWvud/+9tXQGxnuhpECF901N2oKkFoC5T3++OP96rnBgwe7u+5a5H7/+7fdBx984LA5TN1GjBjhLrzwQvfDH/7Q1c1rZgNA+iEEhEBtEUgkvLVF5J8Vl0pD3d8A1b+qCOBsYdq0qcGfkUc2thWdCH7nO99xc+fODf6S2oY6WN1Qb2ADG9fMEUXSc7ouBISAEKg6AomEt+46vJLwVv3VV/3yRiBLHd6kulZZTx+SK6Kb1PK6LgSEQN0QSCS8WQOBNALf7u+//77bt29fsLsYg/HY0Myr0z744IOzhiHX/GiDr7/+2h100EGO5WYkSmmC/1xebZWmnIojBISAEBACQkAI1BOBRMKblQ4vNiI3bdrkVq1aFZjOQefMArYi2WSC3l3WoS52eFnS/eMf/+j+8Ic/uK1bt4b6gaeddppjNzcG+5NILMul2Pd87bXXAn1B9AR/8IPT3QUXTJSFi6xf2BLml4cObwlhUpGFgBAQAkKgAwgkEt4OpN0yCcjuL3/5K7d48f2haSD/ITZb5BXq4FoY/O+4445gssEOdT+89NJLbsWKFW7atGnu1ltv7UF6Icpxzz7yyCPBTvB7771XO8F9QHUuBISAEBACQkAI5IZAolmybuvwQrZ+/etfu9tvv60H2cV8Dn/9+/d3o0aNygUcJLx1CVGya/X+61//GkjYV6/+/+xScESFYcmSJQ5yG/cs9j8xfUQ8BSGQhEARdHiTyqbrQkAICAEhUC0EcpPw7tixwy1cuNBBqiygr4tEEdM7B2XszxQAABl2SURBVA58QziPOuoou61jhxFARxcVBNRJ8EKFSgKqLBs2bAjbBUK7evXTDWoKeKdC+muBdjvrrLMansOz05VXXumwfaogBOIQkEpDHCq6JgSEgBAQAt1AIJHwdlOHF+kuJMs8HFExSNNjjz3mJkyYkHqzVDcAsTTroNJAXcePP8c9//zz7uSTTw7MF7FpDb1czB/ZZGTbtm3BZkJ2tNN2qDvYPdKYNWuWmzFjhnv88cfdTTfdFEDIfeKJ8NobpaMQEAJCQAgIASGQFwK5qDR8/PHHgftLv9KQpqKQXcpVF5UGbJKyKRAyy+Y0bHZefPHFbvTo0WHzQF7/8Y9/BL/ZVLh58+bwHqonF110UfDcqaee2uDBingQZAUhIASEgBAQAkJACOSJQCLh7WahMD2G1NDCiSee6M4777xCSHatTHWR8Fp9o8ck+6QQ3zfffDOMDjHGuxNhyJAhDssaFiDHTG4UhEAcAtLhjUNF14SAEBACQqAbCORCeP/0pz81bHYaNmxYIV16mlSzG8AXOU2IiHmgopyom5hN4g8//LCh6CNHjgx/M0nwNzvu2bPHReOHkXVSewTQ4VUQAkJACAgBIZAFAomEt1s6vCxxf/TRRw11Y7NUkq3XhogZ/qiLHd44SLdv394ggUdyi+oDwbeTbM/iqIIAKY6akqvrpMGw0VEICAEhIASEgBDIH4FEwutL6jpZTKSHeFIrekBaWUeyxoTkmWeeadiUdu655zqzlrFz584G6XyzdoQcl6Gtm9VB94RAJxBA170KIa33xSrUVXUQAkKgWgjksqbYLelxp5vGlvE7nW6R0/uv//qvwGqDlRF1hssuu6xQ+tVWNh3LjUAzHV7Uno444ohSV9Dv5z744AP37rvvlro+FP7AgQOB6cLSV0QVEAJCoHYI5EJ4uyU97mTr1VGlAVfBmBbzzcVhF/mEE07oNbT9+vXr9bN6sNoIJNnhxfbzv//7v1eq8jhp4U9BCAgBISAE8kEgUaXBl050smgMcgMHDmxIEulH0cxX1c1KA/g/99zzgX1ka5wzzjgjcB7hL2MOHz68wfSYxY07os8b1emNi6drQkAICAEhIASEgBDoJgKJEt5uSWEhT2bGyipm5qtsY5Rdz/NYNwnvpk2b3OLF94f6uagy3H333T2sZ8QRWJxVsOkQnWd/UxuuoaOTmzzbVHmXBwFMFXarD8oKBayUmIMWvic2f1YhYFIyzqV4FeqmOggBIVBdBBIJbzerfPzxxwdSQus06UBZRi8S4e1m/YuWNqbDFi1aFKoysMFm/vz5gbvgaFlpI0ivtR0uoi189tlnDfp9EJbo5Mbi6igEknR4ef9wO3766aeXGqRbbrnFrVy5MqgDqkHTp08vdX2s8FOmTHFvvfWW/dRRCAgBIVAKBBIJb7dUGkBl1KhRgScv6zQhT8uWLXNjx44NPH4VAbm6qDSgt3vfffcFboDBHbIxe/bsHqoM1iaQXZxNmOSKyQoqKSeddJJDorV7926L6saMGRN4YAsv6EQIeAgk6fAShZWBsk+AfQk131XZ6+M1nU6FgBAQAqVDIFGHt5s1wcTVJZdc0pDFSy+95ObMmeM2btwYOCtA6pin04I6uBZGb3fVqlUNert4Sjv22FHujTfecC+//HL4ZzvMsVwxfvz4sO2YrLDRDdu9a9euDSW/DPC4LFYQAkLAhd9F2bEo2l6LsuOp8gsBIZAdAokSXl860enioMfLsthzzz3XsDTG8t+aNWtC97R4YFu9enUuUsI6SHgxQYZ019QTaGdUS2bPntVwjeszZ850S5YsCcyT4QYaHUuz5sDuc8iuSX2Jf+aZZ7qzzz6706+O0hMCQkAICAEhIASEQNsI5CLhpZQs77EpCuLkB8gXRIq/Xbt2NWyC8uNlcV51xxOoIPibzAxTnwDbNf+ImTIIMFJcCz7ZZYPOrFmzCqOeYmXUsVgIJOnwFquUKo0QEAJCQAhUAYFEwttNHV4DjiXv5cuXu4kTJzaQJ7u/f/9+x0aoPELdrDS0gzES+p/97GeBdDg6YcGU2YMPPugmTJjQTpKKW0ME0OFVEAJCQAgIASGQBQK5jzjjxo1zK1ascH/84x/dH/7wh2ADFBVn08opp5yS2y5/cy2Mua2qBjYJPv3004H3pFZ1PO644xq8rYELpBfVhS1b3nF7937isNHL5jXiKggBISAEhIAQEAJCoCgIJBLeburwRit/+OGHBxuckPh+9dVXwW2kP77Dg+gz3f6NhJdyVTlQv/PPP7/XVYT0QnD5UxAC7SIglYZ2EVN8ISAEhIAQ6C0CiYS3twn29bkqS1T7io2eFwJCQAgIASEgBISAEGgfgVx1eNsvbnZPmEpDdjkqJyFQLwSkw1uv9lZthYAQEAJ5IpBIePMsVBHy1qa1IrSCyiAEhIAQEAJCQAgIgb4jkEh4s9Th7Xs1Op+CJLydx1QpCgEfAenw+mjoXAgIASEgBLqJQCLh7WamZUkbr2IKQkAICAEhIASEgBAQAuVGIJHwZmGHt8jQ1cG1cJHxV9mqj4B0eKvfxqqhEBACQqAoCBTOSkNRgKmDa+GiYK1yCIE8EPjiiy8CT4Pm3Gbw4MGBG3NZismjNZSnEBACQqC7CCQS3rrr8AI7roU1+HX3BVTq9UUgLx3eTz/91P3mNy+51aufdtu2bQvdaw8dOtTNm3ezmzZtaqEa5cMPP3Tvv/++2737w8DBy6BBR7qhQ492J598clNb4f/93//tduzY4d599123d+9eN2jQoMApjOxmF6p5VRghIAQyQiCR8GaUf6GzkQ5voZtHhRMCbSOwfft2d/fdd7uVK1f2ePbvf/+7+/LLAz2u53UBworrdcoKMad8Fr773e8GXg5nzZoV6zwGBz6rV/9/7vHHH3NvvfWWPeZwBQ6pnzLlJ5rMh6joRAgIgTog0IPwHnTQQYG3s7rr8NL4kvAW5xM45JB+xSmMStIRBNDhzVLKC9mdM2eOe+mll2LLD4nEPXZRAthAVn3CamWD/FKPXbt2ucMOO8zhot0PkN3Zs2c1kGTuv/fee8F1zq+44vJcvVn65dW5EBACQqDbCPQgvF9//XW381T6QqAtBPr1E9ltCzBF7oEA+rpLly5tILsmJR0/fnwY/5hjjgnP8z5BneqUU04JJLyUFZWL/fv3u7/+9a9h0SCwq1atCiS3hx56aHAdYo9k15cIf+973wuf4/rixfe78ePPcUcffXSYlk6EgBAQAlVGoAfhtcpKh9c5qTTY26CjEOg8AllKdzdt2hQQQ78Ws2fPdtdff32oB4sKQdHCeeed52677TZ32WWXuQEDBjisx0BwIe9GaF9//XX30UcfBfq51GH9+vUNUuGZM2c66uqrckCU169/rXD6ykXDX+URAkKgOgj0ILym0uBXkQ60ClK2zZs3+9Wq1Pnu3buDAa3sExVUaT7//POmbROdiGhjYVO4CnszS5WGZ599NiSIADJx4sQGssu173znO4XDig1mJ5xwQkPZIOnr1q0LSS3f/r59+4Kyo4b14osvhvVAMjxjxoyADF9++eVuzZo1IQ6//e2rIrwhUjoRAkKg6gj0ILxxKg3oiiXpvZUZIEi86YYaoecYJVBMApBGFXFANPyR9sRtxLH7ZT0yYPsh2jb+PZ0LgTgEsMqwZcuWhltTpvw0lOw23Cjgj2i/g87uyJEjQ8LLtw/RJTBZZIObBdQgMLdGGDJkSKDva5JhLDiAzeGHH27RdRQCQkAIVBaBBscTZggeUjFw4MDKVpqKMWhY6NfvEDuNPcZNAmIjZnyRdvLrkXH2Xc+OwZrApIQ2skmJZYyt5Ki0N0oOLK6O9UUAk1579uwJAUCfdcyY74e/y3gCUbXApNC+A2wK+yskw4YNCyfw9BeQXguspqAKoSAEhIAQqAMCoYQXooD+F9JMCB5Lfjt37gwkAFEg/A7V7tF5spxeROsOVi5b7ocknnbaaaF0lzowGNigYXXyjzYZ8K/lfY5k5q67FjmWJv0BkHLFtRHX/Xbyccm63aJ522/KaO107rnnOmyO+oE2ipPC+3F0Xg4EstLhhez63wOkD33YsoaPP/44sM5g5R89enQooPCJPffpI+jT7dyfILMB7sCB4phhs/roKASEgBDoBgIh4Y0mPmrUKHfvvfcGmySw6UjHyLKZnR848GXwCHYry9RpmpTwW1WGRskhxDfqZc0GjChGefxmYmJEgbJecMHEYLc1m1msbWgndPrK2kbgSjtF24j6KlQHgax0eHG6YMv4oAfpaza5LTrCa9eudejtWkC9Aak14ZNPGutqk0eL6x+ZBPgTAf+ezoWAEBACVUMgkfBaRaPkz65zhFBBSoyYFMlou19OO7dy8tvUGExaaJJDi8vvMhMsI/ZWH6t70duI8lpZrZ2sLtZGcZMSq6eOQqDKCOB1jU1pRuBRZ2AzmvVVe/d+UuXqq25CQAgIgV4j0EB4o9JDUkVamER6ISIm3YX84rrSpIq9LlGXH/SJLlkxULQiUkiiiqYbaqonNtDFwcY9pL5lC9ZGlNufkMTVw+oPHkVUO4krs64Jgd4ggMrZ6tWrGzYQT5o0yZ1zzjlhcjY5DC/oRAgIASEgBAIEGgivYeKTKdv9a/esQ4Uk+vfsOvE4NyJsz+VxtHL4ZbNyGFHyya7d45rdN3UGBpuikV4rr3+0cvvX/HMfkyK0EWWLax+uW12sjeyaTcC4TrA2KkP7BAXWvwABU83pNhxMxJGEmlSUZXy/7+p2/p1Kf8OGDW7ZsmVhcqgxXHvtteF3wg3qmjag2mHfWNpnFE8ICAEhUFYEehBe06sz0gupiJPyQpa4Z6TDBhDrQO2YNzDRclh5KZcRLYtjRMovs0kNi0SmKK8vueW3tRG6vFYfq4e1of22+3a060U6RtvJymptxH27RrmtnYpUB5WlOQLW1zSP1fe7WJyB3BnhZWMXqgFl8jJGeR944IHQWxoEfv78+W7s2LENAFFXn9z7m1HpI3yd3f79+7sjjjii4Xn9EAJCQAhUFYEehDdaUSMVUULFdZ90RSWHef+mHlYGO/frZvXimpEozn0iZZJD/7kindukxMrdrGxG7onDZMWw6dSRdHubVvTZaD1oq6Q2Iq7IbhQx/fYRwCoD5M4CrnnfeecdN27cOLtU6CNuke+7774GVYYrr7zS8RediPtmx6gU1luwusM3xGTYJ8BsaCuztYpCN5oKJwSEQOEQ6EF4rQP1lxt9cujXABJCJ2rB4nGEDJst3zx+R8tkv/1jlERxz+pgZBcc7Jr/bN7nhqmRXr+MNjmhjNE2sjp2un0MDytXO23vP2vnHP324bdJff26WjvZe+s/r3MhAAJYMRgzZozDna4FnLSMHz8+8EBm14p4RJUKV8L8WcBkJNLdQw891C6FR8yQYabsrbfeCq7hhAKpLnGRbGOKzAK4lEnKbeXWUQgIASHQGwR6EF4S8cmDOV3wSQZxIFWEKCkJLsZcj8br9m8rR9IR8kQdmpGooksObUnYSC91jWunKNbEi17r628f53bT8p+NO///2zuX1aiyKAyfbhExQbyNnMWp6BM4ETHoTNpBO4o4lwQdONIIPoEgOHImPoKzFvMA4sgHEGMPxSuJiEia/4S/emVnn6oTrTqXqm9DuS9r39a3o/61s+qUz0i26J/87voZ5fyhbZtAfFM9SSb69+zy5cs7vlZXgvDWrVvF6upqoUcwOkkYxp8xt7eVK25Xt7sOx1Dc7vLycrkdhTk46e+IxK5CN/T8agteid0XL9aKK1f+KtbW1gYhERp34cKih5NDAAIQmHoCWcErr6PoVb1K+PaVkP9Tc24/fGMoIZUycJ+u5N6fhMMw0duV/e51Hzob3UT3+Yz26vMs9fcbtiZ8PnfuXHH27NkdYQH6unTdgOpG1Eni8syZM662mkvQ3r17d4dIlYC9ffv2rn1dv369uHnzZvlvlp7a8PDhw1IkSyjfu7dafjmNxLPT6dOny+d3u04OAQhAYNoJVApeOZ4TVBZWqQjpIyj7YJFrH/ogdr1Xn5HrzqflDUrujLjV9SmT1yWg20/d5q6vr+8IbVA8r15K+rCXfu3fFcGrEAwJ8pgkYH3bG9vfvHkzqOqDbIrvffToUdkm/xTC4SQ/b9y4UZw4ccJN5BCAAASmnsCfozyUoJLokMjwr5YlEC0SY3nUXF2ye9/2Q/7pJV+rRGSX9h/3ov3qZR9kS/2zn3FcH8r2Q3u1f/a3b+fUB95N7rGpkAb7pA+pPX78uPzadIm+NElIduVRfene9lJXWMb9+/eLpaWlXcPk98rKSvYDb7s60wABCEBgiggMveGNfkpc6AMUEh251FdBJX8snORfn5P9qPKhT2ekc5Egivko/6r8ph0CJiDR++TJk+Lly5dlnOurV69Kk26AFxYWOnO7q03piQsSp/qa8FEpfvmE+sqfBw8elHG6z5//Uz6t4eTJk+WXVFy8eDH7gbdRa2CHAAQg0GcCefVa4VEqONJ6xbDeNE+LP6kfab0vB+J9O+/LvtlnPQJ+M1Ov9/h6SQxeunSpWFxcHHz4Vm8G45vf8a326zOdOnWquHPnTq0JcnuXn9euLRVXr/5dfgZDPjpEqNakdIIABCAwRQT2JHinyG9cgQAEZpyA3kjlHu3VFSza3zje7EnkInS7cqrsAwIQaIvAyBjetjbGuhCAwHQTaDqGd7pp4h0EIAABCAwjgOAdRgcbBCAAAQhAAAIQgEDvCSB4e3+EOACBfhJQ3CkJAhCAAAQg0AQBBG8TlFkDAhCAAAQgAAEIQKA1Agje1tCzMARmmwAxvLN9/ngPAQhAoEkCCN4mabMWBCAAAQhAAAIQgEDjBBC8jSNnQQhAQASI4eXnAAIQgAAEmiKA4G2KNOtAAAIQgAAEIAABCLRCAMHbCnYWhQAEiOHlZwACEIAABJoigOBtijTrQAACEJgxAnNzczPmMe5CAAJdJYDg7erJsC8ITDkBYnin/IBxDwIQgECHCPDk9w4dBluBAAS2Cbx792/x+vXrXuP48uXLYP8fPnzovT92JvrlNuXz89zmRh6UIQCBbhFA8HbrPNgNBGaGQFUM7+fPn4uVleXiyJEjvWbx6dOnwf6fPn1aPHv2bFDvc+Ht27eV25+fny9tzis7YoAABCDQMAEEb8PAWQ4CENhNQLGeUeBK9Oo1LWna/PG5HD9+vJC4nZvbFrpuJ4cABCDQNQII3q6dCPuBwIwQUAyvb3klds+fP1+sr68XX79+HUlAt6caE29RqTfHQ6wPHTpULCws7BC7Bw4cKA4ePJg9v/3795fnvW/fvqydRghAAAKTJPDH1tbW1iQXYG4IQAACOQI/f/4sBdCPHz9Ks8Trx48fi/fv3xffv38vNjY2io2NzWJzU/lG2afq1lcxsseOHSvIJ8Mhd36HDx8e3O4qflc3vRa8Kh89erQcJgGsdglevclB8OZo0gYBCEyaAIJ30oSZHwIQqCQgYWvBq/K3b9/Kvpubm6V4Vd3iVwYJYCWJ4DRJFDt2NJbdL7blyrHNY5S73Xm0RXva7nocF8u2x7xJe921qvo5jCEndiVwFaZisSsfEbzxpClDAAJNEyCkoWnirAcBCNQiYPGadpbotdhKbW53LrvEsep6xXIcm9rdT308Vxyfs8e1quxxPvdxnrPFOaM9tsfxsY/bncumFP1RPdpjOe23Pfr/P/1UhvRmV2K3KnG7W0WGdghAYNIEuOGdNGHmhwAEKgmkYQ2+5dUNr5Jvd33T64l060hqh4AErm99LW4dt2vxm97uaqe64XX/dnbOqhCAwCwT4IZ3lk8f3yHQMQJREEn0xrpElUMe1C4xnOZyx23RtdgWy+4T22LZ9phHeyy7T2yL5bp291OeG19lz/WNbbEc53C5rl399FKKQld1tee+Xc3hDF6LHAIQgEDTBLjhbZo460EAAjsISLgqVcXyyuY+Kv/K7a6EssVZudiQP0b1/V17XHrUXLFvrjxq/Ch7bs66bTHkxALYYles3Saxq+R63fnpBwEIQGCcBBC846TJXBCAwC8RkKC14NUEFrgSbEoOcfDktrtO3hyBnHC10NUuothVHcHb3NmwEgQgUE0AwVvNBgsEINAQAQvYYaK3aiupGM71kyCr089j0/6j6h6nPO0bbbly2j+t58a4bVTf37V7HeeeT3mafINuQWyhy6PIUlLUIQCBNgggeNugzpoQgMAuAukH2NzBYth15f5VvW+AJbZiW1rXmLRtVD2OiWWPy7XVsbmP89w8uTb3T/NhfYfZPE+dPu6rvCpZ6Noe43Z5OoOpkEMAAm0RQPC2RZ51IQCBXQRScZve+EpUqU/buTY+qT1Mcu50z+NaKx6kb3a1lt7EIHYjHcoQgEBbBBC8bZFnXQhAoJJAFL4SvRJRUfxWDsTQGgHd/vqrogljaO0YWBgCEKgggOCtAEMzBCDQLgHdDipZRI1rNxbQmq9Ouc66cZ503knU0zknvX6OgUStz8Y3xxa63OzmiNEGAQi0SQDB2yZ91oYABPZMwGLKgnjPE0x4gH6FH/fY9/owXIQrDKODDQIQ6BIBBG+XToO9QAACEIAABCAAAQiMncCfY5+RCSEAAQhAAAIQgAAEINAhAgjeDh0GW4EABCAAAQhAAAIQGD8BBO/4mTIjBCAAAQhAAAIQgECHCCB4O3QYbAUCEIAABCAAAQhAYPwE/gNhK5/zmtbZTgAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据聚合与分组运算\n",
    "对数据集进行分组并对各组应用一个函数（无论是聚合还是转换），通常是数据分析工作中的重要环节。在将数据集加载、融合、准备好之后，通常就是计算分组统计或生成透视表。pandas提供了一个灵活高效的gruopby功能，它使你能以一种自然的方式对数据集进行切片、切块、摘要等操作。<br>\n",
    "由于Python和pandas强大的表达能力，我们可以执行复杂得多的分组运算（利用任何可以接受pandas对象或NumPy数组的函数）\n",
    "* 使用一个或多个键（形式可以是函数、数组或DataFrame列名）分割pandas对象。\n",
    "* 计算分组的概述统计，比如数量、平均值或标准差，或是用户定义的函数。\n",
    "* 应用组内转换或其他运算，如规格化、线性回归、排名或选取子集等。\n",
    "* 计算透视表或交叉表。\n",
    "* 执行分位数分析以及其它统计分组分析。\n",
    "\n",
    "# GroupBy机制\n",
    "第一个阶段，pandas对象（无论是Series、DataFrame还是其他的）中的数据会根据你所提供的一个或多个键被拆分（split）为多组。拆分操作是在对象的特定轴上执行的。例如，DataFrame可以在其行（axis=0）或列（axis=1）上进行分组。然后，将一个函数应用（apply）到各个分组并产生一个新值。最后，所有这些函数的执行结果会被合并（combine）到最终的结果对象中。结果对象的形式一般取决于数据上所执行的操作。\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "分组键可以有多种形式，且类型不必相同：\n",
    "* 列表或数组，其长度与待分组的轴一样。\n",
    "* 表示DataFrame某个列名的值。\n",
    "* 字典或Series，给出待分组轴上的值与分组名之间的对应关系。\n",
    "* 函数，用于处理轴索引或索引中的各个标签。<br>\n",
    "\n",
    "**注意**，后三种都只是快捷方式而已，其最终目的仍然是产生一组用于拆分对象的值。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A股整体概况数据分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tushare as ts\n",
    "import pandas as  pd\n",
    "import pyecharts as pe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>industry</th>\n",
       "      <th>area</th>\n",
       "      <th>pe</th>\n",
       "      <th>outstanding</th>\n",
       "      <th>totals</th>\n",
       "      <th>totalAssets</th>\n",
       "      <th>liquidAssets</th>\n",
       "      <th>fixedAssets</th>\n",
       "      <th>reserved</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>601869</th>\n",
       "      <td>N长飞</td>\n",
       "      <td>元器件</td>\n",
       "      <td>湖北</td>\n",
       "      <td>19.59</td>\n",
       "      <td>0.76</td>\n",
       "      <td>7.58</td>\n",
       "      <td>965787.69</td>\n",
       "      <td>561101.50</td>\n",
       "      <td>189854.81</td>\n",
       "      <td>155202.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000955</th>\n",
       "      <td>欣龙控股</td>\n",
       "      <td>纺织</td>\n",
       "      <td>海南</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.38</td>\n",
       "      <td>5.38</td>\n",
       "      <td>130058.56</td>\n",
       "      <td>66284.50</td>\n",
       "      <td>44417.80</td>\n",
       "      <td>49573.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002195</th>\n",
       "      <td>二三四五</td>\n",
       "      <td>软件服务</td>\n",
       "      <td>上海</td>\n",
       "      <td>22.88</td>\n",
       "      <td>42.30</td>\n",
       "      <td>44.36</td>\n",
       "      <td>957737.19</td>\n",
       "      <td>540403.31</td>\n",
       "      <td>7009.65</td>\n",
       "      <td>272988.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002580</th>\n",
       "      <td>圣阳股份</td>\n",
       "      <td>电气设备</td>\n",
       "      <td>山东</td>\n",
       "      <td>140.59</td>\n",
       "      <td>2.98</td>\n",
       "      <td>3.54</td>\n",
       "      <td>199927.50</td>\n",
       "      <td>139065.56</td>\n",
       "      <td>42368.53</td>\n",
       "      <td>53754.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002608</th>\n",
       "      <td>江苏国信</td>\n",
       "      <td>火力发电</td>\n",
       "      <td>江苏</td>\n",
       "      <td>16.70</td>\n",
       "      <td>5.39</td>\n",
       "      <td>37.78</td>\n",
       "      <td>4652351.50</td>\n",
       "      <td>864956.13</td>\n",
       "      <td>2432949.75</td>\n",
       "      <td>1310714.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name industry area      pe  outstanding  totals  totalAssets  \\\n",
       "code                                                                   \n",
       "601869   N长飞      元器件   湖北   19.59         0.76    7.58    965787.69   \n",
       "000955  欣龙控股       纺织   海南    0.00         5.38    5.38    130058.56   \n",
       "002195  二三四五     软件服务   上海   22.88        42.30   44.36    957737.19   \n",
       "002580  圣阳股份     电气设备   山东  140.59         2.98    3.54    199927.50   \n",
       "002608  江苏国信     火力发电   江苏   16.70         5.39   37.78   4652351.50   \n",
       "\n",
       "        liquidAssets  fixedAssets    reserved  \n",
       "code                                           \n",
       "601869     561101.50    189854.81   155202.56  \n",
       "000955      66284.50     44417.80    49573.46  \n",
       "002195     540403.31      7009.65   272988.91  \n",
       "002580     139065.56     42368.53    53754.13  \n",
       "002608     864956.13   2432949.75  1310714.00  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks=ts.get_stock_basics()\n",
    "stocks.iloc[:5,:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* code,代码\n",
    "* name,名称\n",
    "* industry,所属行业\n",
    "* area,地区\n",
    "* pe,市盈率\n",
    "* outstanding,流通股本(亿)\n",
    "* totals,总股本(亿)\n",
    "* totalAssets,总资产(万)\n",
    "* liquidAssets,流动资产\n",
    "* fixedAssets,固定资产\n",
    "* reserved,公积金\n",
    "* reservedPerShare,每股公积金\n",
    "* esp,每股收益\n",
    "* bvps,每股净资\n",
    "* pb,市净率\n",
    "* timeToMarket,上市日期\n",
    "* undp,未分利润\n",
    "* perundp, 每股未分配\n",
    "* rev,收入同比(%)\n",
    "* profit,利润同比(%)\n",
    "* gpr,毛利率(%)\n",
    "* npr,净利润率(%)\n",
    "* holders,股东人数\n",
    "# A股行业、地域、市盈率、流通股本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>industry</th>\n",
       "      <th>area</th>\n",
       "      <th>pe</th>\n",
       "      <th>outstanding</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>601869</th>\n",
       "      <td>元器件</td>\n",
       "      <td>湖北</td>\n",
       "      <td>19.59</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000955</th>\n",
       "      <td>纺织</td>\n",
       "      <td>海南</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002195</th>\n",
       "      <td>软件服务</td>\n",
       "      <td>上海</td>\n",
       "      <td>22.88</td>\n",
       "      <td>42.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002580</th>\n",
       "      <td>电气设备</td>\n",
       "      <td>山东</td>\n",
       "      <td>140.59</td>\n",
       "      <td>2.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002608</th>\n",
       "      <td>火力发电</td>\n",
       "      <td>江苏</td>\n",
       "      <td>16.70</td>\n",
       "      <td>5.39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       industry area      pe  outstanding\n",
       "code                                     \n",
       "601869      元器件   湖北   19.59         0.76\n",
       "000955       纺织   海南    0.00         5.38\n",
       "002195     软件服务   上海   22.88        42.30\n",
       "002580     电气设备   山东  140.59         2.98\n",
       "002608     火力发电   江苏   16.70         5.39"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=stocks.iloc[:,1:5]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "eg.按照行业对A股所有股票的市盈率分组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.groupby.SeriesGroupBy object at 0x00000178A9913EF0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped=df.pe.groupby(df.industry)\n",
    "grouped"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "变量grouped是一个GroupBy对象。它实际上还没有进行任何计算，只是含有一些有关分组键《行业》的中间数据而已。换句话说，该对象已经有了接下来对各分组执行运算所需的一切信息。例如，我们可以调用GroupBy的mean方法来计算分组平均值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "industry\n",
       "专用机械    121.122109\n",
       "中成药      46.751733\n",
       "乳制品      59.161818\n",
       "互联网      60.499808\n",
       "仓储物流     65.546571\n",
       "Name: pe, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.mean().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面，数据（Series）根据分组键进行了聚合，产生了一个新的Series。<br>\n",
    "如果我们一次传入多个数组的列表，就会得到不同的结果："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.groupby.SeriesGroupBy object at 0x00000178A9823748>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped=df.pe.groupby([df.industry,df.area])\n",
    "grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "industry  area\n",
       "专用机械      上海      182.131000\n",
       "          北京       67.731250\n",
       "          四川       55.238571\n",
       "          天津        0.000000\n",
       "          安徽       37.238000\n",
       "Name: pe, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.mean().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里，我通过两个键对数据进行了分组，得到的Series具有一个层次化索引（由唯一的键对组成）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>area</th>\n",
       "      <th>上海</th>\n",
       "      <th>云南</th>\n",
       "      <th>内蒙</th>\n",
       "      <th>北京</th>\n",
       "      <th>吉林</th>\n",
       "      <th>四川</th>\n",
       "      <th>天津</th>\n",
       "      <th>宁夏</th>\n",
       "      <th>安徽</th>\n",
       "      <th>山东</th>\n",
       "      <th>...</th>\n",
       "      <th>湖南</th>\n",
       "      <th>甘肃</th>\n",
       "      <th>福建</th>\n",
       "      <th>西藏</th>\n",
       "      <th>贵州</th>\n",
       "      <th>辽宁</th>\n",
       "      <th>重庆</th>\n",
       "      <th>陕西</th>\n",
       "      <th>青海</th>\n",
       "      <th>黑龙江</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>industry</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>专用机械</th>\n",
       "      <td>182.131</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.731250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.238571</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.238</td>\n",
       "      <td>34.862500</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>143.6725</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>191.73125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中成药</th>\n",
       "      <td>33.840</td>\n",
       "      <td>51.14</td>\n",
       "      <td>76.06</td>\n",
       "      <td>36.520000</td>\n",
       "      <td>28.508</td>\n",
       "      <td>112.100000</td>\n",
       "      <td>22.34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.806667</td>\n",
       "      <td>...</td>\n",
       "      <td>47.372</td>\n",
       "      <td>69.77</td>\n",
       "      <td>56.4100</td>\n",
       "      <td>62.743333</td>\n",
       "      <td>32.2725</td>\n",
       "      <td>NaN</td>\n",
       "      <td>161.19</td>\n",
       "      <td>104.65</td>\n",
       "      <td>85.54</td>\n",
       "      <td>20.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>乳制品</th>\n",
       "      <td>11.365</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.93</td>\n",
       "      <td>58.180000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>互联网</th>\n",
       "      <td>46.272</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.756364</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.840000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.870</td>\n",
       "      <td>24.626667</td>\n",
       "      <td>...</td>\n",
       "      <td>274.885</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.5625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.01000</td>\n",
       "      <td>32.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.29</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>仓储物流</th>\n",
       "      <td>191.845</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.703333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.085000</td>\n",
       "      <td>208.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.790</td>\n",
       "      <td>8.940000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.7200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.85000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "area           上海     云南     内蒙         北京      吉林          四川      天津  宁夏  \\\n",
       "industry                                                                     \n",
       "专用机械      182.131    NaN    NaN  67.731250     NaN   55.238571    0.00 NaN   \n",
       "中成药        33.840  51.14  76.06  36.520000  28.508  112.100000   22.34 NaN   \n",
       "乳制品        11.365    NaN  19.93  58.180000     NaN         NaN     NaN NaN   \n",
       "互联网        46.272    NaN    NaN  89.756364     NaN   35.840000     NaN NaN   \n",
       "仓储物流      191.845    NaN    NaN  25.703333     NaN   13.085000  208.96 NaN   \n",
       "\n",
       "area          安徽         山东  ...         湖南     甘肃        福建         西藏  \\\n",
       "industry                     ...                                          \n",
       "专用机械      37.238  34.862500  ...        NaN   0.00  143.6725        NaN   \n",
       "中成药          NaN  32.806667  ...     47.372  69.77   56.4100  62.743333   \n",
       "乳制品          NaN        NaN  ...        NaN  60.17       NaN        NaN   \n",
       "互联网       14.870  24.626667  ...    274.885    NaN   27.5625        NaN   \n",
       "仓储物流      49.790   8.940000  ...        NaN    NaN   11.7200        NaN   \n",
       "\n",
       "area           贵州         辽宁      重庆      陕西     青海    黑龙江  \n",
       "industry                                                    \n",
       "专用机械          NaN  191.73125     NaN    0.00    NaN    NaN  \n",
       "中成药       32.2725        NaN  161.19  104.65  85.54  20.96  \n",
       "乳制品           NaN        NaN     NaN     NaN    NaN    NaN  \n",
       "互联网           NaN   13.01000   32.00     NaN  29.29    NaN  \n",
       "仓储物流          NaN   29.85000     NaN     NaN    NaN  22.09  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.mean().unstack().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通常，分组信息就位于相同的要处理DataFrame中。这里，你还可以将列名（可以是字符串、数字或其他Python对象）用作分组键："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pe</th>\n",
       "      <th>outstanding</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>industry</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>专用机械</th>\n",
       "      <td>121.122109</td>\n",
       "      <td>3.317656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中成药</th>\n",
       "      <td>46.751733</td>\n",
       "      <td>6.880533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>乳制品</th>\n",
       "      <td>59.161818</td>\n",
       "      <td>10.132727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>互联网</th>\n",
       "      <td>60.499808</td>\n",
       "      <td>6.817692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>仓储物流</th>\n",
       "      <td>65.546571</td>\n",
       "      <td>5.957143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  pe  outstanding\n",
       "industry                         \n",
       "专用机械      121.122109     3.317656\n",
       "中成药        46.751733     6.880533\n",
       "乳制品        59.161818    10.132727\n",
       "互联网        60.499808     6.817692\n",
       "仓储物流       65.546571     5.957143"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['industry']).mean().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "结果中没有\"area\"列，是因为该列不是数值数据，所以从结果中排除了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>pe</th>\n",
       "      <th>outstanding</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>industry</th>\n",
       "      <th>area</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">专用机械</th>\n",
       "      <th>上海</th>\n",
       "      <td>182.131000</td>\n",
       "      <td>1.278000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京</th>\n",
       "      <td>67.731250</td>\n",
       "      <td>5.713750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>四川</th>\n",
       "      <td>55.238571</td>\n",
       "      <td>3.778571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>天津</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>安徽</th>\n",
       "      <td>37.238000</td>\n",
       "      <td>1.980000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       pe  outstanding\n",
       "industry area                         \n",
       "专用机械     上海    182.131000     1.278000\n",
       "         北京     67.731250     5.713750\n",
       "         四川     55.238571     3.778571\n",
       "         天津      0.000000     2.750000\n",
       "         安徽     37.238000     1.980000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['industry','area']).mean().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GroupBy的size方法，它可以返回一个含有分组大小的Series："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "industry  area\n",
       "专用机械      上海      10\n",
       "          北京       8\n",
       "          四川       7\n",
       "          天津       1\n",
       "          安徽       5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['industry','area']).size().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 对分组进行迭代\n",
    "GroupBy对象支持迭代，可以产生一组二元元组（由分组名和数据块组成）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped=df.groupby(['area'])\n",
    "xa=iter(grouped)\n",
    "xb=next(xa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>industry</th>\n",
       "      <th>area</th>\n",
       "      <th>pe</th>\n",
       "      <th>outstanding</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>002195</th>\n",
       "      <td>软件服务</td>\n",
       "      <td>上海</td>\n",
       "      <td>22.88</td>\n",
       "      <td>42.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002184</th>\n",
       "      <td>软件服务</td>\n",
       "      <td>上海</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603713</th>\n",
       "      <td>仓储物流</td>\n",
       "      <td>上海</td>\n",
       "      <td>29.76</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603009</th>\n",
       "      <td>汽车配件</td>\n",
       "      <td>上海</td>\n",
       "      <td>31.44</td>\n",
       "      <td>3.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300551</th>\n",
       "      <td>专用机械</td>\n",
       "      <td>上海</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       industry area     pe  outstanding\n",
       "code                                    \n",
       "002195     软件服务   上海  22.88        42.30\n",
       "002184     软件服务   上海   0.00         1.36\n",
       "603713     仓储物流   上海  29.76         0.38\n",
       "603009     汽车配件   上海  31.44         3.21\n",
       "300551     专用机械   上海   0.00         0.65"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xb[1].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于多重键的情况，元组的第一个元素将会是由键值组成的元组："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped=df.groupby(['area','industry'])\n",
    "xa=iter(grouped)\n",
    "xb=next(xa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('上海', '专用机械')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xb[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(('上海', '专用机械'),        industry area      pe  outstanding\n",
       "  code                                     \n",
       "  300551     专用机械   上海    0.00         0.65\n",
       "  603690     专用机械   上海  661.67         0.91\n",
       "  300483     专用机械   上海   69.72         0.61\n",
       "  603131     专用机械   上海  777.98         0.50\n",
       "  603855     专用机械   上海   36.31         2.02\n",
       "  603960     专用机械   上海   83.16         0.52\n",
       "  603159     专用机械   上海   51.36         0.25\n",
       "  300462     专用机械   上海   29.17         0.68\n",
       "  603012     专用机械   上海   29.99         6.37\n",
       "  603895     专用机械   上海   81.95         0.27),\n",
       " (('上海', '中成药'),        industry area     pe  outstanding\n",
       "  code                                    \n",
       "  600272      中成药   上海  58.76         1.60\n",
       "  300039      中成药   上海  15.55         7.19\n",
       "  600613      中成药   上海  27.21         4.79)]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ls=list(grouped)\n",
    "ls[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "dc=dict(ls)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 选取一列或列的子集\n",
    "对于由DataFrame产生的GroupBy对象，如果用一个（单个字符串）或一组（字符串数组）列名对其进行索引，就能实现选取部分列进行聚合的目的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.groupby.DataFrameGroupBy object at 0x00000178A9BCC978>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('area')[['pe']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.groupby.DataFrameGroupBy object at 0x00000178A9BCCB38>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['pe']].groupby(df.area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.groupby.SeriesGroupBy object at 0x00000178A9BCCDD8>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('area')['pe']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.groupby.SeriesGroupBy object at 0x00000178A9BCC240>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['pe'].groupby(df.area)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "计算行业、地域对应市盈率的平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>pe</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>industry</th>\n",
       "      <th>area</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">专用机械</th>\n",
       "      <th>上海</th>\n",
       "      <td>182.131000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京</th>\n",
       "      <td>67.731250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>四川</th>\n",
       "      <td>55.238571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>天津</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>安徽</th>\n",
       "      <td>37.238000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       pe\n",
       "industry area            \n",
       "专用机械     上海    182.131000\n",
       "         北京     67.731250\n",
       "         四川     55.238571\n",
       "         天津      0.000000\n",
       "         安徽     37.238000"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['industry','area'])[['pe']].mean().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面，这种索引操作所返回的对象是一个已分组的DataFrame（如果传入的是列表或数组）或已分组的Series（如果传入的是标量形式的单个列名）。<br>\n",
    "# 通过函数进行分组\n",
    "任何被当做分组键的函数都会在各个索引值上被调用一次，其返回值就会被用作分组名称。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>industry</th>\n",
       "      <th>area</th>\n",
       "      <th>pe</th>\n",
       "      <th>outstanding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>362</td>\n",
       "      <td>362</td>\n",
       "      <td>362</td>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>344</td>\n",
       "      <td>344</td>\n",
       "      <td>344</td>\n",
       "      <td>344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>309</td>\n",
       "      <td>309</td>\n",
       "      <td>309</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>351</td>\n",
       "      <td>351</td>\n",
       "      <td>351</td>\n",
       "      <td>351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>199</td>\n",
       "      <td>199</td>\n",
       "      <td>199</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>345</td>\n",
       "      <td>345</td>\n",
       "      <td>345</td>\n",
       "      <td>345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>401</td>\n",
       "      <td>401</td>\n",
       "      <td>401</td>\n",
       "      <td>401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>361</td>\n",
       "      <td>361</td>\n",
       "      <td>361</td>\n",
       "      <td>361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>455</td>\n",
       "      <td>455</td>\n",
       "      <td>455</td>\n",
       "      <td>455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>409</td>\n",
       "      <td>409</td>\n",
       "      <td>409</td>\n",
       "      <td>409</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   industry  area   pe  outstanding\n",
       "0       362   362  362          362\n",
       "1       344   344  344          344\n",
       "2       309   309  309          309\n",
       "3       351   351  351          351\n",
       "4       199   199  199          199\n",
       "5       345   345  345          345\n",
       "6       401   401  401          401\n",
       "7       361   361  361          361\n",
       "8       455   455  455          455\n",
       "9       409   409  409          409"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(lambda x:int(x)%10).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"hello\".startswith('h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'上海'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def f(x):\n",
    "    return '上海' if x.startswith('6') else '深圳'\n",
    "f('600000')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>pe</th>\n",
       "      <th>outstanding</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>industry</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">上海</th>\n",
       "      <th>专用机械</th>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中成药</th>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>乳制品</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>互联网</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>仓储物流</th>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             area  pe  outstanding\n",
       "   industry                       \n",
       "上海 专用机械        47  47           47\n",
       "   中成药         32  32           32\n",
       "   乳制品          5   5            5\n",
       "   互联网         10  10           10\n",
       "   仓储物流        21  21           21"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([f,'industry']).count().head()"
   ]
  },
  {
   "attachments": {
    "image.png": {
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TTz113cY6zrjjVg0Kbcunn3xSlvrBD8qLL71UP4r3/fkXqEs5iIZvzdj6SjvyyaefFF/sDXuZ7y+0UBW0XuX/8QcfVjKEqDgQGy+T+5xxbboWAkgGQ2halIkmnLB88P4HdWeWLcVwZOzsA5G77Lxzfd/KPH3nLT/+8Y9b32oKIojwvPb66+Whhx8u22+/fZlvgQUqobFExMbFziEaNt92YribLhFIBBKBRCAR+DYQ6PH5gP4DkA2aDeQiBFOQA/oK5MSW1rqHZJCF7Rd6CzYsA6tiiWGkPr1qPHnJ1xM2QYZweC/K5599Vv0JPss+BOmnH35U/ZRN6+JcX+DWs2dd7pG75R87fuTLjTRwU8vAggeU0qt3r9K//4Dy2aBdQAMDRtz//XtU06JqDM3GaNRRRimffWrnT6lLZ3BnAO3e95EQjN59+pTP+39e/W1pbjpaNH2j3/QFLZt0doL5pAFiqd+QzHSJQCKQCCQCicA3QcDO00n6TlLGHGOM0uPz/v0HEDYvvPhifRIOg8j+pUfp3+OL98FVgVaNMgcVzaPpKoHpMdBEpLn8Qtsy6D7IDYGGxISrefcYaMxp+ytBGO6LKzabg7Qr3u0REQad5ckhK4Mu22KMWLdN3CDj3ocCAfdl/OE6KEVEboNLqD5DVKqLPuU3aLnHOBpURFvqvE0EEoFEIBFIBDpGoGebTCePfOy2b9+Jy5hjjlF6VfEeZILwGSTlB4r9QcJrkKCrdwMDvlxa9f9Cu9KK0EZIqsAbRCYi96Zf//Jlg8wokqBspmmVMUiI1nu8qBkwAl/DrWI7CIPYMtwkiYK+QHVQgg4wk1cdGoN2VQXG/Pr3H2gvFMkiLO7znAgkAolAIpAIdI7AF1IDZaCUwAQGvgmslJaNSutJuZFTEISG1ze+rHkOyniw/Dvy66C0wdJ0EJ5egyMwrPBq5tu8Hrz0vEsEEoFEIBFIBIaGwBdEpWdPKy79S48eHqEH+vfob62klJYB5BdP25Z+hpZ5hicCiUAikAgkAolAIvD1EejZeP9ZmHGEqcFII/UqLaISgc2ivuA4Td+8TgQSgUQgEUgEEoFE4NtBYOBCT0d5DSgDBvT8YunnC01KREZTkqoEGnlOBBKBRCARSAQSgW8fgQE9vmybOrCUgcs6LRuVb7/ozDERSAQSgUQgEUgEEoGhIdCZnclQiYoInSUeWqEZnggkAolAIpAIJAKJQBcQGMrizRcvSulCXhklEUgEEoFEIBFIBBKB/yYCSVT+m2hnWYlAIpAIJAKJQCLwlRBIG5WvBNd3I7LX53PNnV5hTN18nw4/R8SL8zdBIfJsltNRfuKJ4zXKXMRXB2HD0kX+ztHm8It6dFS+uoofR8SBd+TDr3kdcZyjX6IM966b9WjGz+tEIBFIBEYEBJKojAi93NbGEJQhUJsCNqISjk3hHEI0wr/qOcrsSrooV1zfivKRw0gfQrsZpyt5fpU4gUu0WVnxVejOyuWPVETaZrxIG/l1VJdmGdobZKWj/DpKn36JQCKQCHxXEciln+9qz3bSLoIvhOC7775b3n777cFIQHsy8ePJXlgIzvZze7r2+w8++KA8/PDDpf1jh+3xmmX4AGXE9wHF+Cozoa78YeW0Nz7QGWW9+eab5amnnmqRt47KFvfjjz+uH3WMcPV88MEHy+OPP95hnQNHffL666+XN954o37g0b3jnnvuKS+99FJk96VzpB/S+UuJ0iMRSAQSgeEIgW5NVEz8cQxHmHbLqhK+hNmTTz5ZLrrooioEn3766XLkkUe2iEt7xQnK9957r2yxxRZVSEvfr1+/cuONN5bLLrusXHHFFeXyyy8v//znPwfrp2afhQB97bXXyl577TXww5eDPmwoHhdn1+rpXrpzzjmnXHvttXX55+CDDy5vvfVWS9iLF2mjvCGdI27kXxM3yo60ykU2dthhh/LCCy9UTQockI2DDjqoYhVtcqaNkpajOdl7773L//3f/7Xqyf+II44ozz///GB+kQYhQka4Qw45pF4jRNdcc02Nf8IJJ9R6KCvSiBv36nXbbbeV22+/vZar7Dhuvvnm8vLLLw+WrhaU/xKBRCARGI4Q6PZLPwQLQRHn4QjbblPVpoA7/fTTyySTTFLrNvvss1cy8Nhjj5WZZ565ZSMhMIQxUkKr8b3vfa+mefTRRyvhmGOOOWocgnDiiSeuhEc5UZY+a7rxxx+/jDvuuGX00Uev3pG/m0jHL5ZJ+NMujDHGGNXvP//5T5lwwgkHSxtLQpGmnYSEfyxtdVQ3acIpn4MHguaMLI0yyijlkUceKa+88kptpzjIzKKLLloWXHDBWn9lvfPOO5XI8efkR5Oi7T/84Q+rX+ASbe7du3e5++67K8b6ZbTRRqtlrrTSSgW5g9n3v//9L6XloV+Qp4UXXrjGq5EG/VP2JZdcUpZYYomy4YYbtvqlGSevE4FEIBEYHhDo1kTFZN6r18AqNgXK8ABsd6sjQfrEE09UYbvLLrtUwUVILr/88uXPf/5zobEgREOYh9Cm0dh6661ruosvvrgSmp/85Cdl8803r00kTE888cR6HcJXPtLR3BDynKUfT//ykjfyoG8J0emnn776WeKg4bHMI+y+++4ru+22W63Xq6++Wn73u99VoiN8qaWWKquttlpNd+GFF5Znn3225m2cIDDSn3/++WXkkUcuK664Yq2Df3CgBaJ12HnnnWt4tDkiSbfMMsvUsuTjsESGrPXp06cSOkRFOm1xRqpOO+20GkbDgZDNM8885aqrrir/+Mc/ynbbbVeXk6TXDnlJp96IDO3Wiy++WMnHeOONV8Ycc8xCI/Lvf/+73HLLLbWeMJxtttmK8GgLYjPVVFOVCSaYoGIa/eY80UQTFX2cLhFIBBKB4RmBIRKVmMCbk180VhihICyeEpthwpv+QTTCz31cixtlyMO9g1C59NJLa7aeMLkos97kv6Ei0OyHk046qay//vot8iCxJ+4777yzHHfccWXjjTduCV5hBDoBOOOMM5Y999yzphtrrLGqULYMwxGwSEc4/eiwFKF/f/CDH9QggpxQVl4I+3PPPbf861//KjPMMEPtV8IZAbEcou9pNZAObRAmL2eahGmnnbaWoyyk49BDDy3TTDNNQaKU67B8Ir6xE+NGfn/6059q/ZZccsmap/w5edHcXHDBBeVHP/pR1Zg89NBD5f777y/vv/9+tSERB2HYaqutyiyzzFLbrq5ImWPuueeuhOW5554ru+++e60bYogwKeeMM86oOKire3Y71113Xc1feuSGlgZZsqSmvLPPPrvce++9tc3yQmw4JER/yFt7xQ3nGokJoug+foPiNONGmjwnAolAItAdERgqUTGZeqLkPFU+88wzdaKkpg6BIIya3D2hZhIkEEzunoSnnHLKmoe83JswpScU+Jl4TcomVU+aMaFa199///3rBD355JPXsLHHHrumUWZOtrVbOvwHVzhG39GYEPyWUvRVGKoSsgjAMcccU3H1tA9Xwvioo44qk002WdWeIBqWjSxl3HDDDfUsLY0K4druCFFaCRobztihGVhllVVaUdliGDOc+hK6yy67bCucjYh6agfCtOaaa7bCmhfatM4661RbEMskffv2rcH8HVyUc8cdd1RMfvvb31ZCEkRKnA8//LDm4To0EUicvLXRmEbK2JxYAkNUIl840xatuuqqtTyaIAQEcQo/AbRKo446ao3jH23PCiusUEmNJRy/BUtHyJx+2HbbbWvcbbbZphx44IGVwMHD70UfIVQMffWPugiL38V8881Xl8ssLU099dRVE9MMb1UiLxKBRCAR6MYIdEpUCA4TXkzEjPquvvrqOoF6Utt1113r5OcpmZGkSd4kOM4445Q//OEPLfJBMHmy9QQsTxO4fD3dy89ywkILLVTV/Aw9TdwMGcVhrGknBGFlkuY/11xzDTYZd2Ns/6dVi74jxPQHUrHAAgtUg01aEP2EhOgTfQhbBrI77rhj7U+CGuaWMGhTfvGLX1QBT5Oy9NJL1/gaiPSwhQin3HDKDGccKavplBHaGHGFn3LKKXVcKFfe6o4EqPNaa61ViYelny233LLMP//8NTuEl80MWxkaB0tZ4drLvPLKK+tSE/KxwQYbVLsS5JdDStRz3XXXLXZEcep16qmn1qUsRIWjBYFB0ylHPeJ3ID1ywa5G3Y1/Tng4WMmL5uXnP/957RPkB5miBZpiiinKXXfdVYkXnAIjvzPEBA7qG6TGNRKEkMpbfOV5YEDe1FFa/ukSgUQgERheEOiUqEQDTHhnnnlmQVRM2ASCp2PaD5PgJptsUo39rPebyH//+99XIULFbeIMQSQ/eZksQ3hQWRMcyAwBZfcDIcHwcN55561r+WwdqMkJTZOs/OSTbsgIBM76ABH01K7PfvWrX1UyQtOx+OKL10yQAeTz8MMPr0ajiIFDXxOaCEFoOuRLSxFLCgRxCD5h0TeE58knn1yXTRRCeNLGILURX39bQuH0q7rScMw666x1OYMGh9ZAvyuHRsVSjnE23XTT1XT+KVd6JAUZs1xI0PNvOukIfmSMrQehTjtk/CljkUUWqUtTdu5EWu2BgzEoDoeQBGmJ/OVF00KLwn7FbwMZZGC733771d+EfAKfSIeI0+4stthiZd999607rJZbbrkazKbIshOjZ6QynDz0nXIOOOCAqu2BN4zZw/j9IIEIkn6UP6f+gX3kledEIBFIBLo7Ap0SFZOhw+RGYCAgJloTuAmYu+mmm6rGY/vtt68ToknRk/nKK69cjS9N3oSPI5z0oVYnXCzprL322jUYOfHkR4C55kJguJZP875GyH8dIhD9Z9fI6quv3ooDc0/iluPCEbqwlYYAh7FrQp1WAKmxdMQ2hA0JDRnSQejZ9WO5L1wIQwQDqdC/HHKLmBK4xokywvZDeJRp6c/BKZ+/PC0LEeYdOXEIbePSWKShIOyVI204SyA0HeqrLexJGLsiKtorPtdM49p41M5wjF4jbvgpHwlEpmCMkClL+y2nIVFHH310xTnGv3rbSaSdDJItddF+KBPu2rDPPvtUrYvfFCcsfk8Ik+UzvxV9oV/dIyrag7ggSuH4pUsEEoFEYHhDoFOiEg0xAbMVCcFmcnWYLNmrOMfTtTRsCSzVWHIgqJoTq/B24eHexB5PeuI31ePSKK95rjf5r0sIBHZwhbE+g28QiMA1BG/E528XjqUISyriW74gHGkzLMPoe0/tluy4EITKIlilc3AEpmWOeLrnR8sjTdTtgQceqMt9sZSBCNtdpN7qQmjT5iBWiMvPfvazlg1KlP2b3/ym2AV02GGHVVLUFNS0J/KRp/hIAuGOUMhP2yOfWulB4xXRoNVQLpwYHiNiTSet8R4kHhkJLGl5lOleW5pEhbZKnmx9aKBoWLxPhYYLybT8w5iWtoaLPF3LR3nq73eK5MjfvTz9DrUrXSKQCCQCwzMCnRKVmBCREILJrgPr8vE0p9EmbxO9ydVWSI6Rn0kWsTFheooWJxxBh8yEU06UFX7NMgiHmNgJkfa4kSbPnSMAMwKMs+PFUlAQE35e4kb71XTSMAR1cPotts8yzAzthn6mCWg6/UT71nSe7hFSAtWY0K8xZvhz+h1J0d/qq47NMP7Sitesf3NcuLZMaAeT3TOTTjppzZsGxbIMe5sw8NVu2qZbb721ai+ivtoeY9D52GOPrUQNoeIQLkasnPLCdTY2pYv3qGhP5COtHU+hoeFvFxRixNhcne0egtP1119ftVnRj8pkeGvJVTvkhXjZbWXpzO+Wxii2Mkcd85wIJAKJwPCGQKdExcQXE7Ytrdb1PR3bXurdDp6uGbZ6Wtxss82qADBhmjgt5XiSQ1hMxNbe7Yig6kdk5MERVp7umxM8UsM/nBeR2X4qLcLEyNKErn6OdB0j0MTGNUz/+Mc/Vs0GTQQhqL+QDFqGpg1E5EhoeieIpQ9P/Gw4vLODISeNGccQ1zUio89DkDb7VPn6zBAfCGEAACAASURBVBO+6xhXEQcZcI34OsIR6saHsyVBY7Ajpy0IEKccy1fGIPsXY49ja2PcIl40FZzxbJcQjUXYhfBHiLSRnRUtD5IRhsfqCi+ED3aWjZCnsFFhAK4+yDt/LtqpLUhJ7ERSV/mxy0HevWvFrjgkC95Ild+P5Sy2YPK0gwjG8tImpCnK0QeIDVKD8MFN/uqv34LcqU9zfNRK5r9EIBFIBLopAp0SlaivydSLtTyhnXXWWVWtbvL0WnUTPpW0Y4899qiTo3X69dZbrzVJer+FnT6ONdZYo9oPWC7gqK09/TUnTfYD8QZSE+qmm25adxjZZWQLLaKSbugINIWRnTrwI0wtiRDElgmQSkSD/ZG+1NfNvmBATWDTOjDetHTCzoIQjBe+IUC0JUiEreQEbgjmZi2RFH0X+cc54rhvpnNPIIfA1QaEiBahGU96yyOxc8e9cEtM3qprjLn3ZlmaFO10H3nY/XP88cdXPGJsIXDIhjojPAS+JaRI5/0riAK7G2EcEiF/xuAwsatNHM49bJB04z3eoaKNtI6WkoRJywjYLioPBwh9GBszmmW3QkNEmyWOt+Y2262+lp/YvCAo+lPZyKb0Qbbasa+VzH+JQCKQCHRTBHr0799/8K0RgyoaE7lbE1tMbtbmaUqEmwjjCdok6doEyQkziROKnPC4NnmKJ49m3uI1y3Ud+TfTiBfl1MzzX4cIRJ/RNtCO0E5x8aRN8CMvBHTgG2n0XxjERuaEN+fJXPzof2mif6O/Io2zcGkJe0RB30U5zXjNa3kjQMYMsmKrMoIc2oOIK16MoxhTzXq55tRP2ojrzC+IkPrEmEJwLGlZ4hqak7+08IVjvMOlWS9l0Qj63egD91EPGkTbwi2VhVGyenlPy5xzzlnjRXv0h2vl6FPXoSWJemqDvlEnZXCu47cXGEX8PCcCiUAi0N0R6BJR0QiTnYOLiTb83TcnwJiEmxNl+8QZcSK/mvGgcjrza07+UZdIl+fBEWjiLSTwgmG4EMxNv4jXTBPEphkWeTTP8uksTvjLy3XcN9PHdYybZv3iOsIirnPkFWFxjjTN+keYdFFf8Zr+kV+Ei9sM76jsSBPx3Lt2uI5wadsxiLBm2vYyhUU9m/EibbNOHV1Hmo7C0i8RSAQSge6MQKdEpTtXOuuWCCQCiUAikAgkAiMGAl+84GTEaG+2MhFIBBKBRCARSASGIwSSqAxHnZVVTQQSgUQgEUgERjQEkqiMaD2e7U0EEoFEIBFIBIYjBJKoDEedlVVNBBKBRCARSARGNASSqIxoPZ7tTQQSgUQgEUgEhiMEkqgMR52VVU0EEoFEIBFIBEY0BJKojGg9nu1NBBKBRCARSASGIwSSqAxHnZVVHRyBrr7sbPBUeZcIJAKJQCIwPCEw1G/9DE+Nybp2DYF2AR9vSpW6PaxrOXYtVuQdb4qNt61K3XwLbOQWb1OVLtJGWJzFaebD331naZTDRX7NMiLPPCcCiUAikAh0HwRSo9J9+uK/UhOC2XdzfJTQ14R9DK9daA+LiijX132V3SQSPq7no3nN18MjE0EgnH3czxe04yN/6ucjhQcffHDNU9pw4kvvezkIkfPLL79cv5Dsa8MRN/KXLshL5JHnRCARSAQSge6DwBczfPepU9bkW0YgtAvOPlh31FFH1Y8E3nffffVr2IS3wwcFg7SoQqRrr85X9Zde/tdff305/PDDWx/Sk89FF11UXnjhhVpE1KNZB3Xy5WNf6G6Si4ceeqh+CHC88cYbrJ7RDl8v3mGHHerXh7fbbrvy1FNPtYhOsxzXymvm3d7evE8EEoFEIBH43yHwtZd+TOxNgdJsQkz6Ed5+H3GHlEfEyfPXQ4CWAP4OX92ljSCU+/XrVzOML2Dzo+mI+GONNVbri9W+0DvKKKN8SYj7EjIC4Yi+lY/4Tf/o/zjfeeedZdllly077bRTefXVV2veTzzxRE3ni8XxdebddtutTD755DVv+T755JPlF7/4Rf36cKBx3XXXlYUXXri8++67lXxpT9RVeausskr1v+uuuypJ2XHHHWvSc889t2pXxhxzzOLLxYjOgQceWMYYY4zIOs+JQCKQCCQC3QiBLhOVEEiEgOs43IcgCr9oX6Rx75rQibhNdXv4Rbo8fzsIwBhxuOaaa8opp5xSBT3SYjmkd+/eVThfeumlhWZF/+iHXXbZpcwxxxyV0Ky22mplkUUWKXvssUclMvoPMfjVr35V9txzzzL77LO30r3zzjuVHMw///zloIMOai2nRN8+/fTT5YEHHii77rprmXXWWesSkOWb5557riy11FIFcUCSppxyyjLRRBPVuijv9NNPL8cff3yZe+65q0ZmscUWK46bbrqp0KpcfvnllYiNP/745bjjjit9+vSpaSebbLIKIm3Ns88+2wLUMtAPf/jDsswyy1TyhqTARPnN8dpKkBeJQCKQCCQC/1MEukxUCI2mIwR79RqYPCb4iCOsGd5MF9cEaLhIH/d5/uYIBEHQD8stt1xZeeWVqwBnm0KjgazQsqy55ppliy22qEJaXHYdnLM+Ouuss8rSSy9dFl100eqvrwh76Tn3+t2yDj8aDOGTTjpp1WgIUxdE6fXXX6+E4L333itHH310mXnmmcs000xTHn/88UqM2MycfPLJZdRRR20RoKuvvrqsv/76ZcIJJywPPvhg+fvf/15uv/32svrqq5cNN9ywjjP5/vGPf6z1UZbwY445pmpYkCHkyvIRAoQMzTPPPGX66aevZchXu9MlAolAIpAIdE8EhkhUCCETv4Mw8fRqYif0PIGeccYZZYYZZigLLLBAbd0zzzxTrrrqqipYRh555HLttdeWSSaZpBIaRpOectddd91qyHn22WfXNGuttVZ9sk+y8u0OEAQBpkEkaBXYpyAnljz0qThvvvlmXRoJYjnVVFPVirjXx5ZQdt9993LZZZdVjYw0obUQ0T2nf2laLOEgE66FOc4555yq/aCpQYAsP00xxRRl++23bxESZIL9Siz/1ExLqRqWX//613UM0dYceuih5fvf/34lX0GU1YeGKOoy3XTTVfKF8Oy11151fG299da13ldeeWUdf/JXVhCyKC/PiUAikAgkAt0LgU6JShAHAs2ujH333beq6KnwPfmOPfbY5bDDDqtPtgsuuGBtlR0dlgR+9rOf1adipOXWW28tP/jBDwrVvKdlyxDIi/R2crA1OO2006qgiTK7F0TDZ20CS8L77bffLoccckh54403qvYAWSHYkckbbrihPP/889VOZOKJJy777LNPJSj6/ZVXXqlLNUjqAQccUPbee+8KhryFc86vvfZa3T20//77V60KMhBEBeFRFlLCPsS9co0ju3akd9D0IE1IR+TrzJ82hobm0UcfLaOPPnpZaaWVqgaHTcpCCy1Uy9TOaDMy7XjxxRerFoftjKUiGhjpm3WP61po/ksEEoFEIBHodgh0SlSaE7inaUTD7ommQzY8tYYjZMYdd9yWICCgCEV2D4wdV1hhhTLffPMVOzJoUhhUIjFsF/hzTSEY+eb56yMAz3HGGacutcjF8sy2225btQmx9LPZZpu1CqB14UI7wcgUefnJT35S1lhjjTLLLLN8aakE2aQt69u3b42HeLINoTVBTNi60OIgqhw/hGHaaaet5RhrloNoVWLpqUYspRIoRBnxYM8y44wz1iDjDHliDOs6NCMxfrST9oX2zxjVrmOPPbYS5ObYjnLynAgkAolAItA9ERjc8KSTOlqusXTgKZnhZbgQCnHffvYky3ASSeEITE/tBBSH/CA3BFC4FCKBxLd3DtIhxyYRQBj0YdOF7ZAw6ZAHRrPrrLNO1YogNxEn0lniE58mhZYM6aCp4fSnMiyzRD3c06ogGeOMO24Ze5xxKtmwlNPe/8iwJSFGrwiTcri55pqr/PznP6/blpWHjMhfeuNOXHHY1tDKbL755uWXv/xlJc6xw8fYU2Y7BrWA/JcIJAKJQCLQLRDoVKOidiZwgm222WYr559/fvnTn/5UfvOb35QjjzyyCgDhTcFC+EQ6Z2HxhO4+hEwIy8i/mUfNIP99YwSafUPzcM8991TjUjtckAbaLloN9iO2DesDAv1HP/pR2Wijjeq9PIJc2N5rp8yZZ55ZZppppla/Wk6y+4ZhLM2b+BNMMEG1T1pvvfVa5EHfyy+OXj16lj7sSkoPA6V83rtPGcl1gzhF+WG3ErY1wDGGVlxxxcJuBVFpkidLRDQqiAybmRhviLJ42vDwww9XQkPz00z7jYHPDBKBRCARSAS+VQQ6JSqERDjXNCG2lhJMDGE9qdKMeA9GOG/+JBSaxKOZT/jHOdK134d/nr8+AjCFPYLAZsguGSQCUbEjRj8hALYG05SJ56DB4KRt9h0tBBsVh7EQwp3NEaNb24jD0bop86WXXqpLQvzVR/40GDRsDGrtDhqpZ6Un5b1+/crHn3xSRh11oPZNXGmQE3ZOlpaQi3anLkgJLQonHaNd25k5Yc12yM9WanY7rmmMpEmXCCQCiUAi0D0R6JSohKAjWAghb/ZkC+Bp1fZWk7+XcG2wwQZlyy23rGp8T6fiEA6cJ3SCIByB4OViTS2L+xAyES/P3xyBEM76ka1IONgLQ1RcW36xRbjdiUOYR1+JT9tiecfW4jCstX3Ze024IKmW+5CX8847r46NyNvyzIUXXljH0/sffFCNXcuAUgaUAeXzTz+vdi3rrLN2WWvtteu2aemQCrvLaGksP8auJKTL2LE1mSaHYW2MWWdjTRzn0MjIjwZm7bXXrluV3bOvkTZdIpAIJAKJQPdEoFOi0qyuN4DakeFp3Mu85pxzziqUllxyyfLXv/61Cgp2DFtttVW54oorqv0BQbfqqqtWQRNCk/Dy8jAv9eJHkOy88841v4jTLDevvzkCMA5sCXDEg7CmEbH84yy8GUc8whsBtdtGGD+OYTRi450k/Bmres9KMw7bDzYi7FnCX7kIrHeY2NKOtHz4wQcD861kpZRNf79Zef/9fmWiiSeuZSEZyre7jKYHMaFdiTxpZNiuTD311HVHD/92p42OCLNLiJO3Nqk7F+H1Jv8lAolAIpAIdBsEevTv3//Ls3tb9UJINb1DWDT9mtfN8BACzXza/eK+mUdeDxsE9ENoVOAeRqz8o4/irAbNvmn3j/tmHGna/ZVnu3NTuzOk1kV+YU/jPpabXMeB/IRGJNI08412Iisc4pYuEUgEEoFEYPhBoEtEZfhpTta0qwiERiGWR6QLctHVPL5qPPnTrFQtzkAFTYdZ9GhQ50gjorpGHYOoxH2c2zMUTxjC0kzfHi/vE4FEIBFIBLonAl1a+umeVc9afV0EQnhLj7BwnQn6r1tGR+mU+1U0Gk0yon7tdRQ+tLpLo41JUjrqkfRLBBKBRKD7I5Aale7fR1nDRCARSAQSgURghEUgF+xH2K7PhicCiUAikAgkAt0fgSQq3b+PsoaJQCKQCCQCicAIi0ASlRG267PhiUAikAgkAolA90cgiUr376OsYSKQCCQCiUAiMMIikERlhO36bHgikAgkAolAItD9EUii0v376DtZw9ha/HUbJ71tx980n69b/tDStdetfWv10NJneCKQCCQCicBABJKo5Ej4nyGAZHxdohGC/5vkMawark7qF3VsPw+rcjPfRCARSAS+iwjkC9++i706lDaF4IxoX5csRPqunqPcKM+5+Vr8zvKJdMIjLb/27xQ10zfTNP07uo48IyzSNv078ov4cY44ztI6mi+4a+YXafKcCCQCiUAiMGQEUqMyZHyG69COBCM/XyH2wcC33nqrPPfcc522UdzmIWLctycKf+eI1x7nvffeK88880wrD0L86quvLnfffXdL+9Cexr2vaz/44IO1zu4RAV92PuaYY+qHCptkQLg69OvXr7zxxhv1Y5o+qOmDhsp3jntn3xKKOkfZlm18+dtr96O8f//73+W1116LKK1ztJuHdP/6179qWnV0PPHEE+Xiiy+uYe7TJQKJQCKQCHw1BJKofDW8hrvYhCcXgtO3dg4++OAqoO+5555y9tlnt8IiTghfBMAhjzjkxS+ErrhRRjN+u/AX/7rrrisHHHBATU8bwu+CCy5okaUo3znq4PqRRx6p6ZTjnuOHvPiqcjjh0olzzjnnlN/85jdls802K5tuumnZaKON6te83YefMyIRecrH9QsvvFC/Ev3RRx9F1uWPf/xjueGGG2q4OJEmyhQR8dt2220rQYq81PPSSy9taVYiLRIUuLUKyYtEIBFIBBKBLyGQSz9fguS75UEw0kjER/loGgjI0UcfvQp15OKDDz5oCc0+ffrUryn7KvEtt9xSZpxxxjL55JPXjwlGXgjOLLPMUsYaa6yWwBZ24403lokmmqimaRIVYdydd95Zll566bLbbrtV7YSvHj/66KO1/Ouvv76SJ3F33nnnMtVUU7U0Gk8++WRZZ511ap2jd6699tqyxBJL1Di0Q/KS1qF9P/vZz8q6665bl4fcv/jii+Xoo48ue++9dxl55JFrHG2Eg3DpYMGpOyI16qij1ntffaZRWWqppapWR3iQMucgRzfffHOZbbbZyh133FHbiETRXtHaIEvi0gStscYaZaWVVqrpoj15TgQSgUQgEegYgS4RFZM4FxNyXDtHWNPPNRdh0rW7jsKafs1raZtlt+eV919GoCl8r7rqqnLKKaeU0UYbrZICpIUgJqQvvPDC1tKLNEjE7LPPXmgTCNdpppmmXHLJJS3BTOhuscUW5fjjjy/zzz9/62vIzz//fCUH/GhJQoCrmb60xPTAAw+U7bbbrsw888zl6aefrkLbUgnCMcYYY9Sjb9++ZYIJJmj193nnnVeOPfbYMu+885Z77723LLzwwmWRRRap2g1LRpdffnld5plkkkkqEYlxoy1/+MMfysYbb1wmnXTS2nbEQ5u1ncYFedlmm21ahCjGmLpEPPVXxsMPP1y1JcgNR3vywx/+sGy99dYtwqHdCNVcc81VSdy7775bl5mQLn69evUqE088cZl22mkHkqNBxKhmmP8SgUQgEUgEOkSgU6IS5CIEXqjqQ8iFQGjmKi4BxUV4TP78hHMRJ+Lxj/KERdoIt1zBr+lfM8p/nSIQGMN12WWXrZoMfUijstdee9WnfJqI1VZbrS6F6AMHbQMHc+SARoAtCHIiL3kgPL17967xYlyww1hmmWXKyy+/XAnJ3HPPXfOQhpbm9NNPLy+99FIZd9xxKwnafvvtKwmigbGEgxjRNpx66qk1f5nr74suuqj85Cc/KRNOOGF56KGHqr0HjcWKK65YNtxww0oyXn/99XLIIYfU+iMDyqRhQQhoUI477rhWm1zQjpx77rllzz33rP4xtmBjaUqbEatXX321khGapb/+9a9Vy0P7pM1sa2h6Ii0S9ve//7387ne/q8QISWIPM+aYY5b33nq73HbLreWpJ58qO+y0Y0Gq+pdB5P3LHL7WKf8lAolAIpAIDESgU6Ii2IRP4FGZe8IkoDxdEwZ77LFHeeedd8qf/vSnKmCs9y+66KI1jcnbev7f/va3+kS54IILlg022KAKQRP9SSedVIUT48ZVV121PoUqiwC64oorqnA466yzqqD45S9/WQVVkBl5p/tqCCAVSAAMGYk6B46WhSJMX8dyh2v9o9+OOuqo2ieWe/RTpFUL1/K75ppryiabbFKF9WWXXVYQFQKdY6Pxf//3f3VZRFzjhuZk9913b+WnrAMPPLASKeMsHG3E73//+zrmaEAOO+ywurS0yiqrtEiVNkgT9Yo6rb/++nWcHXnkkeUXv/hFa/ztsssuZbnllquaI/XhnJE045HBLy3U2muvXc4///yywgor1CUqGh3LSRytS9jHIDZHHHFEmWOOOSo5E454IVKzzjpr+fzTz0rp2aMcdcSR5b333q3p818ikAgkAolA1xAYIlGRBYHlifUvf/lLFSwIiqdLE72nWoKMbcLmm29eJ3dPyEjKoYceWpcRPD3us88+VcDstNNO9Umeap/KnGp8q622qk/Znvppa0488cTWmr4naE/eVPcLLLBAFWpda1bGCgQIbTjTonjCt3RBsCMRBDMtAINPGhTLEjQKhLB0CMziiy9eNQc77rhjawkoCEGU8fjjj1dC+oMf/KAuLVmq0dcILQJAU7HlllsWBEYf03ZY8qHtUA/kB4FVXmh0Im8Cn/ZjsskmqxoVhHnNNdes9jPIyTzzzNPS3EQa9QuSpN1sauSrXFokdiS//e1va3Tjm9N+hG6++eYrU0wxRS1LeyxlwYN2BcEZf/zxy49//OM6RrWP8/tgc8LeRv6R3zHHHlP6Tty39BxQyucD+pfHHn2s/GCZpWt4/ksEEoFEIBHoGgKdEpWmMDKJTz/99OXXv/51zRW5oEmhkrc84Embav++++6rkziNiYm7Pk1+/nl9CiW4aF2o/hk6coQWQXDTTTfV5QnChbBAeggmAuXMM88sDCcRlXRdR4Dw55zHGWec1vIHQoB0ICxIwE9/+tO6rBNxo4RIzyZFfNoyZPXnP//5lwjjlVdeWeacc85KBmgf7CqyFGJsGEfsNpAjWjL5Ii8McdnCBFGg6WFzEhqOqAfBT4uHALODMSY4y0nK2W+//SqR4BdjlmHu/fffX7UsxhSCQqNz1113VfsXY/CEE06o+agXWxN2M8qWhzK1W10t3RinCLcxb7nHMhccg6jE2DT+AzflzjTjTNUQuYeu6NGjvPnGm+Uz2pV0iUAikAgkAl1GoFOiIoeY+E3g44033mCZEjQEAIdciEN9z7EBYKRJYHAm/uYuDktCt956axWUtC+rr756jWeS95TsqTmcJ2H5pftqCMBS/zkHGZADfN1HmL4LF/3tPq6RGcKa9oNGgTErDUOTUOhDfYTA8meLQsuGqIRDCOQZ+UZ6Z0Kdf/hFGmfjDBkx/h577LFKlvgjB8aNbcO2BBuL8tFeGhzaGWW6529suqdVomGKsrTP+BSPH2xgYtypEwIV49H2atu5kTb1QuC5SOc62lcDBt0PqMtLA/PPlctAJs+JQCKQCHQNgSESlcjC5OupMlxM6k0/cUIgOtttsfLKK7eeTE38nGUjyzuHH354faLed999q+AQJo/Iu1lWU5iGf56HjEAITH3B0JS2gv0JTQDhSxuAtFhWsW1YfELbFly2FfrBEY5NCPLJFmOGGWZoCWnLPoiJ3TP6OAQ9Gw/LPTEmIp/oY2UHeVUXREL6IBDN+EEIaDkiP/Fo9GjkEI3wl442z9F02swGxtbnjhwNkzzl9c9//rNqX9jcIEM0MLQp3A477FBtZGgQA+OO8lMfS5a2dlv6GdCjlHHGHvtL7esobfolAolAIpAIfIHAEIkKQWUyRkiaAsR1k6S4jkPW1vBtX2VEy+6BgGFAyfjw9ttvr+v8jC092drxQfBx8nU0BWT4fVHlvOoKAvoNdhwDUfYWCAFtw8knn1z7CzFZaKGF6tJM4M5+iAtCUW8G/UMqGZNK52VqnGUZu2vCyJQfbYfyLP/oZ06fKkMdaCNoPNggcYS68SFfpCDiqwOSYkmF3QiNStMJn3LKKetOoyAzkTbOyqVRUR4SYixaNlIX6SOeetnpY4nSmLUbynKWbcWWHmmHwq5FGsSHZolr5hOESblsVuBd+g8o/l59/bWWdqa0GSXXjPJfIpAIJAKJwJcQ6JSoNMmC6+Y9UmJyDj9n9yEYaVMsBTC0NZnTiNCueDplf+JdGgwi2bAQQAQJF/k1SVD4fanm6TFUBEJoehdKOH1kCzCMCXe7b+xWaXfSwj76wllfEdaWWqLP/vGPf9QXmEkvP/5hX8SWA1GRj8MSimU/2gjLMIiqfI0dab0R1k4bYwNR4O8197ZHM2IVPt1007WqKj2C4z0nUZ9WYBvZkpc2aX+M04grTP1oQA466KBaf37hGI5z0tG8GM/eq7L88stHlHrmz46GAbgdQoi6pSZ5K/uTTz8t++y3X1no+9+vmp0+vXvHJuXB8smbRCARSAQSgS8Q6JSoxORtcqbCN0GHwPHUawcHtTY/T6NezOVJVHwqfHYFJm1bSvnH201pTxhVepqeaaaZalw7KggsQk2+jD/lKy/bUQkA9+m+HgKBnT5FEGgKXNMsIAjCm3GUYmnGS+IQmSAr+oMxLY0JTUb0j751LU9xkQY7f2gT3BPSyqExYXht+SR24ETZ4jC6palAdDhpGdHaiWQMeUeLd5ooS3xjy3KOOnpFfpQvLBy/yAvRMY6VLe8IE+7aOIu2qBe/Ji7SImnGLpJtR1SkdaatoTVEtrTFfRMXhstBdOTdP7UqFb/8lwgkAonAkBDo0b9//04ZQAiR5tNqTLwxyUecEA4EkjD3EUcFIp5z5Oc64oSAi8oqh4t8xU337SAAc/gSms4IC7/oiygl7sWJvmrG4x/9E4SEHxd9HPfGBWLR1O5EOR2dI53lGgQCGY6yhKmPMzIQxq7uo86RZ9QbabLzhyFs2MJEfhE3zpEm6iBe+Pk2EDsfWqhoozAOEbKshWgPzcGL66wOQ0uf4YlAIpAIjCgIDJGojCggjGjtJMxD8Gp7CNphiUMQiK9a1pDSRVhX2vBV4g4JhyHlE2FftY1DKi/DEoFEIBEY0RFIojKij4BsfyKQCCQCiUAi0I0R+GIxvxtXMquWCCQCiUAikAgkAiMmAklURsx+z1YnAolAIpAIJALDBQJJVIaLbspKJgKJQCKQCCQCIyYCSVRGzH7PVicCiUAikAgkAsMFAklUhotuykomAolAIpAIJAIjJgJJVEbMfs9WJwKJQCKQCCQCwwUCSVSGi27KSiYCiUAikAgkAiMmAklURsB+98ZVhxeTNY9hDUWUFeW4b74kzX27a0/TDI+0TT/XkSbyq/c+Cyj/Dt7A256+/V45nZXVHjfulfVV00kTb8ON8mrd23AJvzgr03U411+17Eib50QgEUgECiBtqAAAIABJREFUuhsCSVS6W4/8F+oTr373+nZHCMdhXXSzPMLUa+TjVfIhaOOsLq7VLYSu+7jm74OEPgPQTNNsg/LEH9BDXqV8PqB/6fd+v/LPJx5v5SN8aM4r8x3NspXZWblRd99VivYNqYxoV9Q30ihPO5vluBbOX/xIG+2Is08G+LZSukQgEUgEhncEugVRCQEwvIPZ3esPZ0LukksuKe+99155/PHHy5VXXlm/WRNCb1i1QdnPPPNMOffcc1vl+YbP4YcfXuuDPKlDCGv1kIa/LxCfcMIJ9YOFUb9XXnmlfoUbWWl3hLd8brjhhnLqaaeWkXr0LCP17Fl6j9Sr/O3Cv5Xjjj2ufpzRd4Tiw4ntebiPcals3wl6/fXXyxtvvNH6xo/wzpzyjz766HLppZfWfDqLx19c7TniiCMqufBdJB9u9PFD3zhqOmWKr43aDp8jjzyy3HTTTRUfGAvfZ599yt13313LRmrSJQKJQCIwvCIw+CzY1op4Wmt6d+TXDO/oemhp3nzzzTrh+vJsc/KXLt03QwCGBFc4gs8Xqpdaaqny9NNPl4cffrgst9xyNTjiEWwhpAWEoGv68Y/+aS8j/CNO9Om1115bHn300TLffPNVTQghe9ddd9UP+T355JP1C8vqN8UUU9QPB0a+Dz74YLn33nvLhhtuWOvpHyHsS9wTTjhhyy/KlS93yCGH1Dzfefud8sH775d55p23nHvOOdVv2R8vWyaYcIJKBPbee+8y/fTTt9ojrbyi3kjdGGOMUYkWUnDQQQeVSSedtKXtET/i1oIH/Xv77bdb2DfDo57ODvW97rrrygMPPFC/Wi35j3/847L77rvXNrR/yFFe+ur4448vq6yySnnttddqHgjV9773vfqlaXWebbbZBmtHs255nQgkAonA8IJAp0QlhJOGxMQa1zFRhr9zCDlxhDfDmulcc8IdJuktttiiCqc//OEP5bHHHqtP3dtss00VDs18XMeEH8JUXuEX5cb9wJJG3P+BERKAINBgvP/++1WbMOqoo1ah+Nxzz1WtCk0LfL///e+XCSaYoC51eCpfZJFFykorrVQ1MXCVHgH4+c9/Xqaeeuqahr8lmF122aXMMsssZf3112/Fjz6jJbjnnnvKeuutV/70pz+1tAEvv/xyJSH63VKF5ZJdd921lbfeu++++8qaa67ZGlfKQ7Ymm2yycsUVV1TigTjMM888tbOFH3rooWWmmWYqc8w+eznrzLPKr3/963LVFVeW7bbZtiyx1JLl8MMOK5NNPnlZZpll6teXYaWuQXLkEc4XkRGoueeeu6yzzjrl9NNPLzvuuGMd89E+dUe6gpxID1tnuMdyDsxmnnnmFm7Kk8fNN99cVl999XLVVVfVL0KPOeaYlcDpA22Hn7hLLLFE0Xd+b65pecYbb7zy6quv1t+QPK6//vrSt2/f+hXn6Ff1iDbFOdqX50QgEUgEujMCnRIVE6HJ2xHq55iUNcgEKE5MesKa4eEfZ/k0XZPYWP830XPOnhCdRxtttBYBauYT5YRQaeYrLN0XGgG4EZhXX311GWWUUYrljnfffbf2m36lVfE0H30944wzVi0F4nHNNddUQjDvvPNWDQJc+f/tb38ryy+/fPGkH31Bw3H++eeXKaecsvz0pz+twt8YUb7jtttuK4888khZYIEFKqEQl3Cl0aEF0NfIBg2BsSGt/t1uu+3KjTfeWJdeDjvssEosll122fLSSy+Vcccdt9Zde2jlLCtpk+WWf/3rX5U43XbrraVPnz5VoE89zTS1vc88+2zp1at31cjIQxu0Xz3//ve/l/vvv79ixX/00Ucvd9xxR/n3v/9dbr311hrX8s/BBx9c1l577TL55JPX4SZ8p512KvPPP3/rd4GEwdv4lpc6jz/++OWYY45plSnxP/7xj4rDJptsUv7617+2fls0S3DQfu1C4uSvTuedd17FG6G5/fbby3TTTVcJpjL9fuD9xBNP1KUtxGa33XarJFI7o8/yd5IIJAKJwPCAQKdExWQWE5p1bxO6CdJT48QTT1ymnXba2j7Ch2DxlBiTfTTcROmJz+Q68sgjt/ITTkg99dRThRCUbxAXgm7rrbeu5UV+Jvhnn322ljnJJJO0JnmaAkIOqSFcPM0TBOo9ojtCFw4EHa2BI9z2229fCQcBuu6665Zf/OIXEdQ6wx6eiMnOO+9cTj311BqGPFiiayeJyAtNCu0HewlEhhNPXrQotAK0C8jRaaedVn71q19VbZr+Y2/Bb/HFF69EKcYfDY4lDtoTYw/hYvthjPzwhz+sZRDg6qitBLoxJez/7ryz7LP3PlUTclND2PccqWd58IEHy0wzz1TTS2f8KVN9XTvUlYYDQVE2IkHoG/vq3HRwmmuuuQoyFW6//farZQcWL7zwQsUhwpWlfex0Pvnkk6pFOvDAAyO4dX7rrbcKAjLrrLNWPzjKc8kll6z2KfpR+RtssEGN9/zzz1cSg8CpA+0kIqMfOGMjXSKQCCQCwwsCnRIVk1lM2H/+85/Liy++WEkBwsAIk5AxsZvAEQZr6p4opTNp7r///nXSBARBs8cee1Q7APdnnHFGOeWUU+rTKOJhWQLh4C644IJy4oknVhsE9wQgP0THRL/xxhuXVVddtQqUfffdt0w00UQ1jPCjOVCOuqjbiO70BWFIkMHQ2RM+HPnDlOAndAk/BMTSAc2LtPrck7glnXPOOaestdZaFdIgkG6MEcL2zjvvrJoC9zQaBKkykADCm42HpRnlIBOWU5QjnMbDNTKMHHDK14fiIcEIgrMlEmMAoQ1HwyEfaTiaGU5Z000/fZlr7rkGI9Hq//4HH9Q04qmztI52UgcDyzW0FAg3ItV06hhpm/6ule+3EM59kyy4Nl6NfYe+0X5LX5aWwv6GBkb5Rx11VMVEfWHrN6QPLWvByZKU/lt00UVrPP0J9yA4+ZuInshzIpAIDE8IdEpUNCImfpOiHRR2i8wxxxxVuCELhNhJJ51USQWbBZOkpQNEw1Mg4z7O0zjiYmJFStiiiGNCZZtAiIZTpok4nKUChp/jjDNOFVLI0NJLL10nZk+hBJf0nrjle8ABB9T4hF9OzAP7kBDXH3a40D4QmCGckRTLJuI4moK0Cvrppis0MPqaloL2qx1XywzIB4Eo/0033bRqSAhKea644oo1nf6RVtmEMhLA6XN1058x5sKfpoUNE7sZRJQ2g1DX58IIaXk2x03c25Pcq0+v0mfkkWs5lboOKr9HY4w1y5Q26mgZhv2H5ZXFFlusEjBtpLVrYihzvxG7kxA2ThtpDZF4y2IcMi3vcPJAftgF+X3ACiGDjWU32i6O3ctqq61Wr/WPvNnm0JjsueeeNS1DY2H6wYOF30Us40V5eU4EEoFEYHhEYIhEJRpEmFGnIykc8mD5B4Hg5pxzzjrBIh0mR5Oss6dwEzNBaAI3kV500UVVRY6kcJ6STdYmZ47QCMEhLQJCE8BOwKSvLp4uPUFKow7icJ7iTz755GpPwE8+TcFQI41g/7SfoN18881bLbcUQEgiFTAj7Nsd7AhEQhIpJbT32muvqh3Rj9FH0lkesbRAUCMUBO4tt9xSVlhhhZoHo1ZaF+VxhKm+s0QhH0dHRCXizj777NU4lHbAWOAse2y77baVADEqjXyEabN79Xzk4UdKn959KgnSZmXT3hirwjty2m3MapelmLPPPruOeXY0duJYqqH5aKZHVCx1HnfccbXN8lCGNvJXJ8SM3Uk4edBSRV/EWF155ZXr7wRR0V64wpCTr3iIO9KEmDOe9XtDDBdccMGKjd+e8uyM4iLvepP/EoFEIBEYjhDoElExyZn4w5lYTcwEUjiTdtybXE30tCcxmf/+97+vURk5mlC5Zr4xkTbLce3J8S9/+UudqG25VIbJmotJu94Myk945BXnCB/RzrCHIU0E7PQZQuBJnHPvKZ+dEazEt/xCmLpu4kcg0owwvJ1qqqlafY04Wv6D+0YbbVTzJVhp4BAVZXAxNtTH+LCcYkwog586HXvssZVQ1ASD/tHgENgEs3oSwBythneNuLcbhyaiOS7EsdREu4M4I9mWhGg5bAPeaqut6jZp8ZQfWLnmaDhs27Y0qY3wo1FiryJPdUV6AiPtRPjYsYSz7RnBhwNHA8QwNtJEPL+nqDu/hRZaqGocEXJEUdvt4pHOIS5/7UDgLVfBxpIbg+KwLXJP86RtnHTRzig7z4lAIpAIdHcEukRUYvJuNiYmv6ZfXJtYTeph0xD+zoSHraVc5MvgNmwOmpM42wNGk5aQPDUr03bZKDvOkb+08ox8w39EPDexsMMGmaARQBIcnubZN9B8sFmBGYFsWa35vpLAjsaEbYj3iITQFEZw05RcfPHFrV1a7F4YcRLutDlc9CthSRuGPLG9CCKDQFmeojXh9K06iR9x4hz50TKw8WDsHfGF2QVjzCibhke+CIp6ulYvxqk0RMiDZUvjVXnyoUVB2GLpJerjbKnFMpixzY4EEdE2GkYkBSGLeru2nMWJg6DTlgQWNWDQ74BftE88bWNsbjeR96HIk3NGbCwFsWFBZti0IC3stbwPhkPc9HOzLu2/lxox/yUCiUAi0M0R6BJRIcBiaUZ7TKoESzwl8wvVvWtP1ibx2AlhsiWcTOrsWGhIPG3aimpJyK4ey0mcNJYJOBO3CdeOHpO3p/kIE24iVrdw6qMe7YIgwkekMwwIKcLJEkk4GCF/cIafd3TQEHTkYB19LB+CmKaEgTO7Ec4TvH5FXsJ5wrdEYquvrcqc+iAdyKclEdoIpDUEMPKAwNLC0baE1i12rhDClv7YjER+kVbdjANlOJAq2hrEjDYFWbEjSVgsNwVhgINt1lE3u4doaxCtcMIc4bzbxHtaaCssS4VRsHB1inopr+na75th8KCFDHJh6zM/BrK2YzNiR1AQOfWTFzJj6U7b2MHYMs2xCfOmW/2ub72gTv/ASbqoX7P8vE4EEoFEoLsi0CWiYp2bCj6cp3HGhTQnnIlv4YUXrk+V7j2VeynYWWedVdfaTZIhsDx5mkiF//GPf6xPhp6KLRdwbEsYF3KeahnrEgqWKAgXgtWEy1H5W4YIhwypF03BiO5CGDk3hROtAtIJQ4LPEgInTsSTRh+zHWpqRPjpKyQz/AlTQpVDaqTVD7Y8I6DhQkh6Zwhyg3BYBom+FI/AjxecebEaR+MhLtJhDMY4UQ6tjLfder29pZ1oK0GOENH20JxYtoq3uwrzllu2HE2n7fJXb2MZMQk85BUEWRuFIXuc+ke7Iz/14JAvRKgrTl9oCw0XHJB0eSsXIXIgMZajbEPWh34TCIzfCU0PHNjV0LJ4HT9yp39phvzebJ/mol1dqVfGSQQSgUTgf41Aj/79+w/+2NdBjWLiNcFxce+63W9Ik2B73PaimmmHFLc9rP2+Wa/2Mkbke/1GcBJoyCDtBZshmq0QwNG3cW7Hst0/7qMPAt+mv2uEg/D0lI90dMXJ07tVvM02lkUYpiId7glpW90RVlvSkSZplGdXGe0aoU47w99hmUj7kasgI1GXqLP7yMcZ4bakQnMUceIsvCMnnP0IYk5D1FG8KMPyEA2OpTU2MUNz0iEgSB2tiqUrmFreOvPMM+urA5CXyN+uLPY5TVI/tDIyPBFIBBKB7oLA1yIqKm8ibk6+7fcRJxrajNsMi8mUX/t1pG0KhWY5Tf9m3PayIizPAxGAmyd4gp0LktKOTxPrZljTv3ndHsd99IVzEANphubEjzQ0C46oL7IlL0czL+0IF8TGvbTiOUea0II000fa5jnqwK8znJrx47qZLsqPsOZZPE49hlaXSBd115Zw0ba4j7o28xSnWa+Im+dEIBFIBLozAl0iKt25AVm3r44AIcYRdCFEQ6DF+avnOuQUISCVx3W1nBCu4jfTyC/qHu1oCm7hzTRx7xzXzfyGVHvxOedmGUNL09VymvlGGufOXISpf/t1pJc22hc4NfOLsKZfXicCiUAi0B0RSKLSHXsl6zRUBEJAizgiCt0mIWleDw24rxJ3aHlleCKQCCQC/w0Ekqj8N1DOMhKBRCARSAQSgUTgayHwxSL310qeiRKBRCARSAQSgUQgERh2CCRRGXbYZs6JQCKQCCQCiUAi8A0RSKLyDQHM5IlAIpAIJAKJQCIw7BBIojLssM2cE4FEIBFIBBKBROAbIpBE5RsCOKImb+66+ToYSP9N8/g65Q7vaRKzjnswxlPi0zE+6ZsIDM8IJFEZnntvGNc9Jv/2YsLf+1i+jmCI9PL9Ounb6/NdvW9iE5g1/b6r7f4m7QqcvkkemTYRSAS6FwJJVLpXf3TL2nhPSfNdJV565s2vXX35WXujIi/nrubRXof2PL/t+6jjt51vV/MjcNUhXtYGJ35dxSvK+Sa4BQZRl8gr/KOMjs4dxenIr7O0UVZH4R35Rd5fZ1xG2sj3q5Yd6fKcCCQCwwaBJCrDBtdul2tMvkM7R8UJJ45g9F0ZXx3mpPe9HN8JagrNiF8jdeEfgeKjfU899dRgJCiSNutJWMfHCCO8/dyRMG3mEdft6eK+Ge6ai3PEGZbndvyibBjD3gcJYQaLr+J81NAXqLloY0dn5Ucdouy4VwdfQPdBy2ZendUj0kU+UXZnH2hsr4/4PibZ/GJ7Z2W1+/t2Fdcsuz1OR/ft8Tura0dp0y8RSASGLQJd+npyR1UwGcWPuzl5tguvuI/Jq6O80m/YIgB7xOLxxx+vX0VWmq8fE37xxO7bPz6IN99887X6NfrskksuqV8iPuGEE2pFfUV5t912K7///e9bXyE2FuShv2NcdNaqCPel33nmmafDaD7Ud84555Tll1++fo3Zl7Y322yzMsUUU3zpmztRz4ceeqh++FD9HJamYmwSeksssUT9YJ/4jqiHsy89+5aQjwi+8MILtezf/e53g301vMOKfgPPqLcsoh/URb2REtcOH3T0gUNfDv8qTtrLLruskjwfPHzxxRfLPffc0/pmkrwIZB95nG222QYjKs16IKq+Fn344YfXr5nr5+23375Vx6h7/NY/+uijcuSRR5aVVlqpzDLLLLXKvujsK+jbbbddmWCCCQYrC/m56KKLij5/6623KinyxepNNtmkjsf2Nsc446+e0Z/K/+tf/1q/OL3VVlvVr0erG//o7/a6+rClDzlutNFGxZe1udtvv71+yXrnnXdukUPlpEsEEoH/DQJfm6iorh9/CKbmpNtsypNPPlnuvvvuOsnGRNYMz+thj4BJlhbk6quvLmOMMUad3K+//voy11xzlXHGGaf2I+EyxxxztL4QLE1MznfeeWf5xS9+UZxvvPHG+qVeQn3fffctP/rRj6pgXXbZZcuMM85Y8+pKi6655pr6dePpppuuJaSlizHlC8f/+Mc/yuqrr15Jx5xzzlkuv/zygjyoVwieKMvYeuyxx2peviZMCzH55JO32mAM3n///TW69CGwIv1zzz1XBdZhhx1WBd3DDz9c+vTpE8HD5NxsR3xwUUHqFk47kQhExeGr15391iKNc/Sdc7SD8DcGRh555FY4AveDH/yg9r18Hccdd1yZddZZy+KLL16zRCgXXnjhKsgRjW222aZibbxw0igHgUD49AUyglwaI8rff//9KyFSnjgIYbTZGbEca6yxyn333Vdmn332mg5ZU98HH3ywxpW/MpdZZplabswn0VaeCDRCZXz7QrY8uGYc90i6r03zp3U6+OCDa7uQsqOOOqqss846Vauj7hNNNFHNI/8lAonA/waBb0RU/Mip5Am/mHSazTCREG477bRTFTgmja5Mss088vqbIwDzFVdcsR6R2xZbbFEndQKl6UILoW/1HyHhqXuRRRYpu+66a3nzzTfL3HPPXZZaaqkqUC0HXHfddWWUUUYpM800UzOrDq/lixRdcMEFdVxEJP4h8PjRpvz2t7+tpIj/yiuvXJ/Gn3jiiTLDDDMMJsyl5TyRL7300rWuNEiejD35c4Tc66+/Xq+bJCXSEsTI06233lqmnXbaMu6447YEfE006F9z/Eadm+Ff51o+hDdtACH94x//eDDBKnzJJZesBIIGivBs1mNIZfpdhrBGTI855pgOowc5Upb+RTKUQQt16qmnVpLioUN+4h577LH1PP/885cNNtigXlvK22effeq4obFzbLrppjWNOcKyDOIiD2cYK2O00UarxEDFEIXJJpushf25555biQJtH4zOPvvsSo7Fpem79NJLq9ZLPtFWbTBO9L9rYxpmSBaCJK4lx/3226/mLQyBWnfddWs7ETkk+cILLyyINM2KfKRLlwgkAv99BLpEVExMMdmpontCzI/5iCOOKBdffHGtuQmBiycd1yYhT+1+6F/VxcQgbaSPCTXKECfCIn9+cTTrLVwdm/lFmu/iObBqx0BbTc4ESbsTl1APTD1dhht77LGrwPQEHn2jHxCPuI+47Wfhkeddd91Vg6eaaqpWuhg7yv/b3/5Wl6GQI046gg5xsXzg6dd9tC/GAqJCoHGE5w477FBuuOGGWmdLCp7YOfEjrbqHXQPtkLYb1562LZOoFz9C0NN8Uxshr2iTa/GiLk3/WmgH/2Ai/r333lvbRZuw2mqrleWWW66Fi2TiEZiWgGiFFlhggQ5yGxhPXBgigmeddVbVWiGaloA8MMw777wdplWP6ANamz/84Q+1bSeeeGKhbSLELRHpOyTwV7/6VRXu4403XsVSuX7nhxxySBXwCCzyinjoK3WWPxzXWGONOi/oA+XefPPNVZMy+uijV3Ih/tNPP10mnXTSMuWUU5bNN9+85v3KK6/UJaVowMwzz1zraCxzSIw6ItHwl796ISHKka97B0J4wAEHlCuvvLJqc/SrZSBk27iUpm/fvpUgiy8veXalX6N+eU4EEoFvB4FOiYofJ+cHbuIzQXl6oQZdf/316+RlUnrkkUeK9WDCy1O7HzItijVnk0FM3pHfV6m2NPKzvn366aeXX/7yl2XCCSesWVjKQJA23HDDOhlTEVP9e9pea621WgLFExk1tknIpGnCNWFq13d50gnsgOXJkDAYddRRK3aECCG81157VcGhjzj+lkvYnsBHf7P7CFW7OHvvvXc57bTTanz/lMPQkjBx3Sy3FalNoNPSsDUJFwKS+p9GQ/7qoGx9hExwhIn+pQ1SD0/e0hqDVPkEqLgEv3SrrrpqFTjujQ39b0mJFul73/teK2/lKRsRQ6xhQ/ipC2xoDREEBEq+lmAQH4KSfQOBbLnJbyMIULRtaGd4EciWnJB+91w7jsql6YFJZ0RFOv2mDosttli1/6E1gJPfid8QrY22GQuInbiEMvuXhRZaqN7zg7M6IUd+M7DXPgfCAEPzgjpx+kEdYejM6RPY0dDAURw4IwycchyWXuApHFljJ8VuBXHhpw5IkP4TP9z0009fHOHYYGk/rUnEU97UU0/dqhN/9XMgrnB2WCpEoqT1O1Df0NDI37007f0SZec5EUgEhh0CnRIVRfpRctZ7DzzwwPLTn/60qv5NPgzPTLCetkwWJjA/ZBOhCdcar2UCKnyT3DdxJr/zzjuvloEkcQTELbfcUgWkp2aTFBsJdgzxhGoi/fvf/17Tmmg8rXLqOSK46D9PiJ6qTdraTtghfMgezAgGThwCwQRt4ibY2CNQ+XP9+vWrSymLLrpozScmfWdCBcYw78zFJI946lNOWsLFgVQaL4Q+jQBVPwPQ0AK49rTMHobx40EHHVTrj0DofwdDWO0jjAm5OBBVZcjzhz/8YX3yVjatkifr5piwLGEJieDmLCOoM/y4f/7zn1XjwH6GpsOSkXF2/vnn1/yU2cyveR19Ih/+7j25c0gW0tOZQy70y5CcNnFBmmiLkDS40VAhlco96aSTquYGYdOv4nNRV79d2CCgfvvInHbyRyoso1m6YRPiLN1tt91WNSFIjN+8fGFGy6HuUTd5KReZ0n796dA3DLals2RkTFlujHS1go1/7VgiRtdee21hXxR9gBQbw7Q44WeciocAITHqipwoz73ytAeRO/nkkysxZBSsPGMoXSKQCPx3EeiUqPihxkTABsFk9Jvf/Gaw2pnwH3jggWqdL8APn7W/yYVNAWcCMDHFBDhYBkO5MSmYNExyDOMY9SEq/DztekoUx5q+3SGcp2j3BFY8NVPjm2QJgZiEvk59hlLdbhXcbB9BH8JeJRFNKm5PxgsuuGA10GxW3oSuvxkletrWxxwBRYjDlb8J3mSuLMLIkozrGDfNPJvXyEwzjj6UzjICDYC6IcW0ZITfnnvuWZO7phkidBjXevqVznjjtt1227LllltWrZC0BKGxwxE48v/JT35S740D5aqHPMIRdp7cCaxwhHuQCX7GES0AYUpbgZRbLlE341B+Mc6UMTQHb5hIF3WJc3vaJm7tYZEm4rDhUD6Cjvix0QgDWWSeBrTZTvVQb3VBZNnDIE/8jAVaFpolBtW0WpyyAkNbipFEWilEUR+w/UESkYNwjHqNnagvf+PJ3IFQ6Vc7vGhQkaVmvMijeY5wv3M2MkEwxWFrE+RZvIiLwCFGyIk56qabbiorrLBCJTnRd84IGTIPx2YbmuXndSKQCAxbBDolKjH5KN6EYbcFAWGZx+TDmVz8mMOZxEzgDP/CUQ3H5BB+XT0367DKKqtUzQgha0I0uSBKnImIXQG1sad/Rn1IE2eiJdSaJKUGjAD/Anc4EkI0JbCBFcFz/PHHV/U8KMSJs2sCwkF7IB1nyYP9hKUgpAHJcSYIGVdG+noxhH+0NggBF2TBdSwxheAm4BCgcJ60CUCO0OSi3vIzDiIc2aANCINJ4yGWDeUf2DjLw6EunrQtS7GFoVVBeo1pAi2c9LSJSAqHDAlHjrmok7w90VtiUy/COZZimnGiLjXxEP7ReLUbPzejN9uBDOhnZERdbfX1e4WJa5oOxJMNih1dlrE4GDgQDks/jG/9xmmtaBUsszFEteyqPYhP/LYj8X3PAAAgAElEQVSQSwfHAJeWxlxA+xpzhbYikBwcwx199NH1d+r3Cn8PJnBXdmiyjMNoY6SLs7hIBZuWplMG0h1OfvJBhMLtsssudWcVTaF+MpbEs0QV2kblBuGJdHlOBBKB/w4CnRKVmMD9YC3tsHMw+XgfgydnJMEkEBOu6oZAa/qZ9OTRnIzjuhlvSM0Vj4rdk7xlKE9OnppMJPL2FEU9b8mA5oBwaOYd5cV5SGV9F8NgEZOsZTnEjdCmDWnHKTDSt5GGEIYpgWS5jRBDBNgiWQpgv8S2ZWgu8vakztCTU374u9afxgxHhY8kh1PfEFr8mnVHYuQTSxhsLgjocAQljUpnTpmIBs0DAW2ZiUEpoUpwBQGSXjlIQ7OuzbpEe8RVB+8gkb84SNrXdZbqLDcNySnbETZilv0QPmSKhsWSG5JHsCMQ7LnYmhkXQcYQGL8pO6bUV1sRVv4eEmiYaCDk5aFFedoGJw8Llu+kUQ5tLDLLXk0d4IBMOKRzb8nM75fGB2FhnxI7yMw7DGtpM7SDk659TrniiitqXZFHdRGHiyWdetNYbjN+tQEmCDksLFsb57Rj6m8sIXCBS+SR50QgEfjvItApUfFjd3CElkmJupfK1NMUomLyFhbO+x78qE1OYQ8SWwQjL3FpPRiyUfE2/SOfOMdkE/fU6mHQ6MmQIyj/9Kc/FROVXRmeqkxOkTYmNXGVFf6R54hwJgw478cgEOLFbSb7WBqBC+wIDWQ0cEIMCCcY63PkENFglMqok5bFUzt7liHh2wwLosLegsBqjgF1VbZlA+QCqQpHuEZbmmmEE5KWAy3HIFjCjUXkRju9RwWxUl/EgYs6KY8NCn+7hRAaBwGOnCEITaLSTBd1cw7MIm/xCMDYvRRx+Q/NRRniyZctGG0DLdaQnLjSImp+Y2y7tMFvDjmkIYGhOPC1ZdlDAA0bzQI/hIF2IbQJ4u++++5VaMPQ0o844YI0sHux/OW3GVoTcWiRLMm1C3zpHIgBY3wu8oq8kSPpEWvjMDRsgbUz7RGSsccee9RkTeyk1/5w2mIc0XIZMzvuuGNtv3Bt1+exlBhpnOXJRbnNsLxOBBKBYYtAp0TFD9KkYdI/5ZRT6lM0FSrDPDsuOBOwp0+7Ahg3eueCyd6TaOycQCRMmJ5Q4mnYhMsQ0nq0J7qu/vgZ3DHeQ4gIO84TH0HJX/melJRj0uGo/EO4VY8R5F9zYrUW70nVWX8FOdEnbHcIYdeEWoTFZO/sCdf6PeFNyxHLD2E06ek3CKu+jLKbUEcfCyOw2DAhl5YUjTP++smBpNgWTUDR0ulDwoU2LWyfpIl+dU2QsUdQV2M2xq6yxKMRQLSQq6ifp3RxjVVj1li0RVlaYaGRQVSkDWdsEYBRBn/jLsac+6ib6ygv0nd21sbO7CAYjFv+9PTfmQuMhfvNheOP6LDpiuUq9Ys+44/IcTRTflu//vWva72ldVgqkRYp0E6aBw8EMGK3hMBarol0tJ7hXDN0Dq0b3PQLWxYu/F032wALy02Ig3L0g51ogaf60+DoO3ZCdhdF3xnrNGS0MbFlXd7SIC60RcYzssJWyzigRVNXhCiWmdQJlk2i2qxjbUD+SwQSgWGKQKdEJQSH0tkO+IH7UXsisxTkniGe5SA7bfyYTSwme+rSeD26F0KZZE0E8nRQA5t0GUkSgNLG5NNZa4VTxVJRE0ohjEwo/AhZE+d6661X18Vpa9SRILSWbgIbkSaYwBo+bIdMtAxK3cPCYVmFmt7k757RoAm/2Rf6HPGjIdOnJnJPtfoZUfFETguhb74Kvmw//vKXv1QNj/EU9WUzgTwRPggpZxnDODMOkVv92iQC4hB8YTvVHENnnHFGFcKWDZFp7TdO1VW7qP7t+kF0aezkLUz+iBKjU+OThiBwUY6lEfGi3tLHzpBm+V29lg9MlRtjlZ97AtrY9kr6qMPQ8pWHtNIhVX4XHOHN/gZxiN8dgR0ETZ+yYeGiHggUMkObo80wjGVD1wS/uMYAjR3NTDyUyMcYsnPIb5ZDdBjFR3tgzglXV6QnHO1epDPfIEzK4mdcIzH85NXsV+PaOPL7N19EP0WfIS/eocK2Cg6ceniICm2RcjjvnzFnBR7VM/8lAonAfw2BHv379+9QD92cEE14TRc/YD96RzgTBWfyanftE4VJgTqa2rk5qbWna97LI+oS+QkPv2Zc1+oTdVFeZ/Ha030X7qP/mn2k3xxwEB4CoL29sIpwT9CenqnmCUxPqk21PuNmRIKRLUHVVYzVixbCclMQFXUjSGnuaMrcqwdB6Tr8or4x9sRxcNFe964REcKHRoRAjjwjrSUf4YSgNDRLxoxw9jg0goxqaTKk5aKNcS9dYOk6yq6Rv8K/qJNxG/lJTvhbQkIilBnxhpa1eIQ5skKTJK3+YofjAcKymjjt+al/ONdIjSW6zrQ58m2mifEjj8hfeODmOvydm2kZ6iI+tCwRp9nmZnyER9/BJsoUzkWe+lL6qAv/CHOWrhkWv42oaw1saHqiTuGf50QgERj2CHRKVIZ10e0TSlfLM7lE2q6mGdHjBV7NCb+JY3t4R2FNP9fhIq37pn+ED+kcaZvp2v2EhdBw7Yg4Q8o7wppxm+W0hw8pTNxmeEd16Mgvyviq5/a8og3NOnQ1z/a0cd/epmZ+X6f8ZppmGc182687ak+k7SisPX0zbrN88Zph7ekibsRpD+/ovvnb6Sg8/RKBRGDYIfA/IyrDrkmZcyKQCCQCiUAikAh8VxAYfE3nu9KqbEcikAgkAolAIpAIfCcQSKLynejGbEQikAgkAolAIvDdRCCJynezX7NViUAikAgkAonAdwKB/2/vPqB1K8rzgQ/qSiwoSxJEmjQBFaliQToIAhYUpUgRkI5U6QJSBIkgRYkUpYoRDYhgkBaqFJWmSBE1GjUGSBEjmBhcEf7rN3+e49zPc84959yD99zDzFr723tPn2f2N+8z77x7Tycq06IbeyM6Ah2BjkBHoCMwPRHoRGV69mtvVUegI9AR6Ah0BKYFAp2oTItu7I3oCHQEOgIdgY7A9ESgE5Xp2a+9VR2BjkBHoCPQEZgWCHSiMi26cfyN8NGrsXxUa/w5TzzFSPUZyX88H+yaeK1m/NjbrOQzWtqR2jhamtkVNlJdR/KfXfXs5XYEOgLTA4FOVKZHP46rFQQ8oeKLr67z+fFxZTLOyMpTliOfNFfuoN+gsGs/cZ64znHiD6YR1pbXphu8Hi5t8s45OKXe8W/P8mnbk3xTnjDp4y+ta0fiDHduyxi8nlna5NeWOZiH+zbeSHH55+us7ZeC2/TaOFL64crtfh2BjkBHYCwI9C/TjgWlaRSHsLHH0iKLLFL30LH3y9JLL12FFSFDaD1bzt4+9gqyaWXKsSGiPWyyoSD/CLuc7VVz77331o3hbD7I2bzOBoZ267XXS/ITFkJh3x7x7J2Ttokn3B4wNqOzW3TS5ty2n589juw3ZNNBQjr1auO5Vo7dxW2AaF8caR944IG6w6/NHt3nkIdDXUJw1NcOvt/61rfK6quvXvfBGixjsGz10kYuebsWTxvtj+Tc7h3U5imNvYTsQrzZZpvVPNrwXKdcGwz+9Kc/rc+MPXk4m//Jx6aRcYmf+37uCHQEOgITReBPdw+caE5NOoMW1werBpTZeJn+UAWC8TOf+Uz5xCc+UYXT3XffXT760Y/W2iWefsu1gLYfx+ufZkt3ww03lOuuu66cccYZ8S6XX3553bW4zdd1ykQMCHs7ZJ933nlD6WyUaANDJCdpk0Yk6ZSnrYsuumiNo+0hGggBwbrvvvtWTPiP5G688ca6a7TdnNsy2vjqgPR97GMfq3XNjryIi3pss802bfR6rczHH3+8nHTSSZWsqBPiZINEpGc0lzbD8pvf/GZZcMEFa/T4q+dPfvKTuhOwnaq1PWGD+dqYUT9svvnmg0Ez3KvvXXfdVT7/+c+Xs88+eygMUUF8W6KirJGwGkrYLzoCHYGOwBgQGJGoGGQcGdiTVwb7DETO8RMnA6Rdd+3YmnBhrh2T4TILTX7OGRhdZ2B23YaJk/tcp25JP1g/8edEBwPOLrMOs+onnnii4vTiF7+43msbAQtPjoC1m/Xvf//7OlOmRVhiiSVquGeBduOmm24qK6+8ciUJwUw+tCME5hve8IYhLYFw6bjvfOc7ZYMNNiiHHnpoFfx/+Zd/WR566KFaFmFuJ1z5HHHEEVUrkfrbvXe77baru+rWjEqphMcOwLQ0hDoBr02c8uSjDcsvv3x5xzveUZ8HYfy52267rdAOuI9fDXjmGW6fe7i1Owe3z5Y0eY4QBnWiEdFGaX7xi1+Up//wVNlj992LvRwfe+xXZautty7vfve7y1NPP13j0Aqp/xe/+MWy5JJLlve+9721Kp/85CfLHXfcUWiRaE5gfthhh9Xy1EEdnRGEFVdcceiZTzsuuuii8tvf/rbepo1Jl3uBNCMLLLDAsDgkr8TXxg033LCceuqpdVdlae+///6a/sEHH6x9oR8PPvjgMhqxS7793BHoCHQEZobAiEQlCQmwDPwGZGpk5wzWrg3k8XP9jW98ow7WX/rSl2o2Bjnh8uFcT4bLQC2v1DECN/cpJwNtzvxTD35pT1vXkKHkMSed0zb9cf3111dtBGFIiBDg/An2K664ogoabYUBkrDccstVYnPggQdW4nH11VdX8qL9CA8hZFY933zzDfWr5aRdd921CkyEpe0bmCIb9913XxW0BBhNw3/8x3+Un//852WttdaqAhtJok2Qr7qoIy2Ksghi7VhjjTXKOuusU2699daqaVE3hGPeeeets3xlOeIQGeWkLxOGSFsWiYNXwlxbhuGQNv7B07V6IUfayCXdV7/61fLBD36wEnT1UfZll11WFlvkVWXFlVcqz3/+C8piiy1aFl9iiUpSnn76qfKC57+gtlk+LRlyT9Ox00471WUWhE5fceqSstVDW7QRZnHqRFuTeMLaNiSec5uu9ZeH/LXXtXIQPHWCj/5UBjKmX/Tb3HPPXduD3Mo32LT59uuOQEegIzAeBEYlKhnYDEpmvwYdgorAM3hxIQbCxDewbb/99sWavPsMkAZ8LuldJ/8aMIEf9fqLv/iLmtKM08wz9RKWMtus+StXOoO4a1oGAyznnr96jra23+Y5Fa8jIPTHRhttVGfw/OBkqYe/6y222KLsvffetQmtwHK9yiqrVCJhaeKQQw6pcWATuwce6cOvf/3r5Z3vfGcVWuwdpBWW/jj//POrFmWeeeapeFuSoe1YZpllqo2EuhDMF1xwQe3HPDc0LTQOhCA7lWuvvbZ8+9vfLu973/vKbrvtVp+/f//3fy9/8zd/U9ukX6NFmH/++WtZiBN/9Um+4qy00koVA3XMcwQjwveYY44p++yzT9XstP175513lkceeaTimfxgQvNhWWuvvfaqmqZLL720ap9e+cpXlid///tyxx13lvvvu68ceNBBlVT9ARF73vPLF77whUpAkEb2O+rx5S9/udYNzkidZRX1sgQVzD2z6m1ZC4GxDCat9jkSD2GCrWdZuLZZnqJF4ydfBCSkKP9nmjN9o9/F4/7u7/6uEk710nbLTuybllpqqdrvbFcQVrZD2pN61MT9pyPQEegITBCBUYmKQcxgyD7AYGXGd9VVV1XhcOSRR9ZBj2Axm9tjjz2KtXAD2Mknn1zPRx99dJ3t7r///nVgv/3228s999xTB+GDDjqoLjOIr5zxuNSLGtxM+9FHH63LEQbQo446qrBfuPjii+usz+yfytxgffrpp1ftgUF+/fXXL7vvvnst1oBL+FBncwZaQlG7x1u38bTjzxGXEOUIkZATJEy7hOk72g5ChSBjCMoJF7bLLrtUGwrqfljzb/vM80G40XbQwCAF7B0QlZRN8NKgICbiIgmE73777TeE729+85tqT0LjgzRKq5yFF164LvsgnYiFPllttdWG7FoigKP5UD8aGKRBHq94xSvqWZnaR3Dzd2Zvoa6Wld71rnfVdvuBB4Gu7EH38pe/vJxyyinV2DV5ie/ZevOb3zykiZFu2223rcL+Kctqc81VzjrjzKrREjbX8/4/jmuuuWYlI+pNU+E/hOhrl/8WwsDpuzgascMPP7z2qXjIEFyRD/VTL/3p2vLPmWeeWTVZyy67bCUQ+lQecFAuHLRpxx13rCQR7vLTD/qXQxBvvvnm2kYaLI4RMzKnDsozCTjxxBOH6lwj9Z+OQEegIzCLCIxIVAxgDo4K24zYmrkBzVq6gZ3x3QEHHFDV8IQUQWYW+7Of/WxImBEgZqEMBT/+8Y9Xo0IDMdJjtmZQHK+LoLRscM0119R6bbrppuVTn/pUXa83ECNJ55xzTkGSLA9I8+pXv7q8//3vr+TJbNwAzx6AVmGTTTapRomEqYFdOzMAB4fx1nN2x08fIgFImLdDCCiChgBiX0D4wJEgJNSPPfbYKqCkRe6o9IXrX1iHQLRtswSgDHGRIMIqGi3l0cYhjIQmgaZcxAmhTR0RCTN7ceNSB8+evkJ2aM3YnNAu0Oy95S1vqW0SN3Uj8GlcCPP0M6FPgNPMIEMc4kJ4W6bg1A0uyDT7Kv3PyTvOM7TCCiuUr3zlK5Xoeq4QX8aytCrJG8E499xz6/+BjUopc5X777+vrLPuOsmqkhfE0IHowYpxMzsXz6X2OAad/5R+EpdGhj0MB08kTj76U71psNSX7ZD2aZM2t05fqLc+8/y3DmHRRof/eiYA+gG5p22DI6dc//P0YYtbm2e/7gh0BDoC40HgT0fBYVIbwKigt9pqqzrLZItgIGQQ+JrXvKbOHDPAS27gyuDlXnpkwIxY/B122KEaahr8JjKYJY3BlXA0C6UCNyMkhAkNRokIi8EUcTI7NPizgTBTpmWh4eEICjND5ISNxsYbb1ztJgzsc7qDsZn1aaedVgkm9T1s9B+s9Athx54ImSH8ORg7kJWPfOQjlax604OAIrzSB+LSsiGphOB66603JHCTD9yFZybO39IAgtAenplBzAk/Sz233HJLJRAJ1ybaFQacBGP7LCHBiI1nkm0HY08kjYCnKZOGFgShQpDURXqkAC7ieD04TljrvMbr2UHKpKE9YtAakiKusl/1qlf9sX1LLlH7IfWv+T2TrbdzLN/4XyDxjHHZuwTrtmzXytRGpMt/S/sc+kWdGC17pRqRQSRoGmErnbaoQ9sm8SydXXLJJbX9ykgcaVz7j3mFXB/qe2UhnJ6ltg89PzO0cbDy/b4j0BHoCIwTgRE1Km0+Bh6DZpxBiuA38HMZzKjLh3MGNvHjCBYzcE6YQdNZOc5c/OrNCD+D+YpG6MSpH+FBGHFm9IQeYUTQWM6IYwdBOFx55ZVVkPEfSx2SfiqeI4yCqTpqO/+0TV+2Tl9y6QuCkPA57rjjKpmzVJGlhaRj2MpOBIGVjnElTQ1tR7QSCEfqo0zPAEHLqZ97cRMnedMIWOJjaIt0IFoc4mNp5bOf/WwlUurYtlMZyAJNgiUKz4Xvt8iH7YVD2+Sjzpz0lkGk9bYT/+BRIzyDC8Gs7paXaBaDpXObFzLl9WkaFXmrw1NP/ZH0PG+uuSpusGUnhCBpxwknnFCJITseeXDqnfblrCxkASnjp65rr7121Uwhl55xdUVUxE26YOw+2kYaKJoVZJXmhEu7xHMNF+ek11/al3FAH7sOBklfM+s/HYGOQEdgggiMiahkgGvLyKDV+hEMgy6DXAYv4a4NqslXXgY9fm28wbwG79tBU1gG0MRzL0/CDgk566yz6rIOrc7xxx9ftS2Ja5atbMsfXsFkw5L0iTMnnYMFjPOaK0FpRmxZgkAh5KjykTfxkDrttlzCwSN95LVib+fQYtBK6TOOPRCtlaU2+XsGFlpooSoALbuFqCSfnPVL+wxI50i9U7Y4BCAXew3X4jEStkSD9MpX3DgaBGVH2yItQUwDZ4lQWwlkGOSZoz2y7JePsyWvlNfer7vuunW5BlFB/uDZ1iHX6vSH8nR5nv/G8+aqtik1H4TleXNVQme5BWHyRg2iwiEX+oomzKvDyJ/6ceoLq2hJtMEkgTaFXRHNJe1ibLaCJczUK+l96M3yKDw4RJOtGe0a7WkbX3j6Kzi3bRSuTsLShzXT/tMR6Ah0BGYRgTERFQNbBFPKy8x8uPtBEjMYdzA/NjDsH6zNe4tAeoPgzJx48oozQCqrdREiPhpGaCEpBnPqcWprjo2BpR8DtZmy2agZNYE7pw+66k/zwJ4IpoQfsgE7BMCSBWNS8WDZar7ct+1nqCwfyyjsQDjGqPqsNUaluWBASztBq8LJx6EOBCuhi+CEnOgD5CGCuiZ6hpzQ2NCM0DjkuZAXoWhGT0BrS+qqjexMCHevytOosG/xnFnqQlqES8Npp3q4F275qq0vQhDHX3ykzbMExwhucVI//rRMnjnx+SNUIW78OMtIcfJOen40gepEm+IaPnHseTzD7Ie8cSU/z7hlI0uf3uxCcGBhmajNWx1opxjEWx61bCq9ciwV+S/AFNnhpNVGcXKtXeqjD11zcOWnf7vrCHQEOgKThcCYiIrZYjtYK5xAaQdog5PBnhPXgBbXhvHLTDfh1Ntm6QQmI0GzVekd7cCd+DkbINUtTn0GBZ2yDda0BAbn97znPXXGyaAweVN3MwJmaMsRQoSyNznk37YlZc1JZ0I+LqSR0HFNOOVNn8Rxhk1m7O4JXksMe+65ZzWsjXBiQEoDwclPOlh6Awb5DFFRnjRe22XYShB6pVa+0kjLEHPLLbes+YVYIRcXXnhhJZCEsreF4hCmhx9+uC7XtcIR6WFvQQuEbHp9VjmEvvp7vnyRlnG3pRFG3bRunkNaGnXlpPnVr35Vv/Ca9nrGPA+Ev0Oc/A881+robSY2I4xYg7c2ajNNniUjtlCe1Qh//x1xHRx/z6OlNvkrB2HjLy7tEE0YogNvpBt5UYY2eqYRIzYvDMUREnWAAe2iJTQY0KC17ZWX514blMcIOlssSO+Z8ME/mjj4e6WaEwYXdUBilbnzzjvXsDn9/1Mb0X86Ah2B2YbAmPb6MSAZQGNTYGAzkzKbNYAbiNxTNVP/G9w5M9nBMP5mh4SJGR+XwdcMjwGimZyB2eDnGMmZsSISWcc3CNOWxJBSHl5dVk+CTDn/9E//VAWzdAZVYWbeBugIO+kIUUa3IV8j1WFO8oelGS/Vvlk3QUdL4dsf+ld4hK5+820N9igtSRVHX+tbGCIKlsuiKYCHOJYiEES4uvcMIQTeUCFMPTfqEiEmDsFN06JP9KEwAt0bR4gE0mJZzls/+oUGwbIWkksoIiX6WBvk1zoGw4Sqj8WFPKmT/GhQaJJSF2fpPQe0C5ZIfFdl1VVXHYrT5p02e359lZemBR6eR3nEeb7YxcBNnds6ukYQtD1aloSnPvJJHROWvJ2RIP2CiHk7iWM0zJgcIeH0HaNxSzsMyuHV5iX/ECP2P2xeGNLz0x7kBunXBn2oL+Pko4389Jf+6K4j0BHoCMwqAmMiKhnIhhskB/3cjxR/uLj8MlhmgBwrORipHKC09RjuPsC18Qbrl3SJO13OcIM5ockROlzwrDfNfXAZjDMcdiOlRYQQy7EKL3k71JHQdx0yRGB6RrTBEW1HSEGISvJwVn7ixV88Lvdpf+6FI7M0JyG6iZN25swfKRa3NTxP+OBZGYNOW/krV/tGKivphKeucNDGkG15JR/xhXNpc+vXlpM8W7+kVRbyOkhMa8bD/AzXxmGida+OQEegIzAqAmMiKqPmMIuBGWidDY4G0lzPYtY9+QgIEDyOEMIW71ZAjZB8Qt7K0LcReuMpBwFJfGd5ccnTtXxbIZz44ggLyWnzSh7iJn78kmebrs1feOtSl9StDRvrddqmvoN1GimPlNe2n58jZM51W/eUkzzbtsfPOXm7Thzn1K+N2687Ah2BjsCzhcBsJyppWAbFDKIZGBPez5OHQARNizX8/xyYj7ecPBdpferIP2GEcK7FSxzX/Ee6HylN0iVtmz71GO6c/MYaf7g8pE0+w4WP5Jc0KTt1j3+bLnFav5Gukz5pBu9HStf9OwIdgY7AZCEwZYjKZDWo59MR6Ah0BDoCHYGOwPRB4I8fnpg+beot6Qh0BDoCHYGOQEdgmiDQico06cjejI5AR6Aj0BHoCExHBDpRmY692tvUEegIdAQ6Ah2BaYJAJyrTpCN7MzoCHYGOQEegIzAdEehEZTr26nOkTXkD5TnS3N7MjkBHoCPwnESgE5XnZLfP/kYjGTkmUhtpvWbt6K4j0BHoCHQEpi8CnahM376dI1rWfohstArnOx6JI52PsfHvmpWg0s8dgY5AR2D6IdCJyvTr05m2iHBvj5kmmKQIbZnRprT7/YxUjHSDzhdm7euUbQDa8LacsVy3aft1R6Aj0BHoCEwtBMa0e/KzXeV2RjycUHq2y5+u+bdfoNVG2BLsds/dcMMN64aCNml897vfXSFI/OAxeM9fX+VLsPJzn3P6se3DXDvbIO/2228vO+yww9Dn+0844YSyzDLLlE033TTF1rO8UpY9guz2+6EPfaju9iwvm0ba/dimhDbyyxKQMHsBPfnkk3VDQBtk3nzzzXUDPnsa2YQwWwdIYxPMt7/97TN8Yn6GivSbjkBHoCPQEZitCEwZokL4RahF4M1WZKZB4TAljJ3jCOvrr7++kpOf//zndTfphCVe9sOJQM/OyuLpG/3kEK/1S3xkSHjyS/433HBDLe++++6rOyvblfm73/1u3cjv7rvvrpvqWc6x+7BduOO+//3vl5/+9Kd1R9743XXXXWXFFVesJIWfstTH2cZ5dv5Vjh297f5Lc3P55ZfXnYSXWmqpGg+JufTSS+tuw9mwMPn3c0egI9AR6AhMDQSmBFEhXOxSe/DBB5dDDjmkLL300lUgTg2I5txahKQQ2Pfcc09BDP77v/+7/O53v6u77CIDwmgrOPHXXXfdsphwAREAAB2gSURBVOCCC1bSgFjQdiyyyCJDpARpueWWW8oKK6xQSYB0IS833nhjJQ6IQfyEIy3Iizrsuuuu5ZJLLilPPPFE1WwgC7Q6//qv/1q1IP/zP/9TDj300LLooovW+iAfd955Z3nnO99ZyQYi47jssstqPb/yla9U/8UXX7y89a1vreW+8pWvLMcee2z59re/Xb71rW+VAw88sLbv1ltvLQcddFB57WtfW+9hseeeew7VtRPkCkv/6Qh0BDoCUwqBmRKVVuCk5gTP4KDOj6CLa9NlZp3wpHd2cNT1V1xxRRVkyaOfJ45A8Ifvr3/96/Lggw9WckI4xy6EBgRBfOihh2pB0hD20oi31157FQSAJiKGqwjG/vvvXz73uc+VVVZZpRIY+fzsZz8r22yzTXnjG99YtRTRcMhT2ttuu6088MADdQlm+eWXL+eff/6Qjclf/dVfFRqNhRZaqGy99dZDdZHHvvvuW+64445y7733lpNPPrkSKctW6oFo/eAHPyj/9V//VYnLqquuWusujP3Ko48+WrUr2j7PPPNUYvT4448PgQqX9hkcCugXHYGOQEegIzBlEBiVqBAyOaLWT80N8MKyTMA/cfgJyz0SYjZP8JiRc67lEffSl760LgHw727WEQi2+mHNNdesR3KlYaDhoL3YfPPNy7bbbpugobM+XHnlleuSy+mnn1722WefGqZP55577ko+eKQcZGaDDTaodi9IxUorrVSJq3D9j9i41r+WcdjJfPjDH67ERjgC8eUvf7m87W1vK/PPP/9Qvup/7rnnloUXXrhY7qFJOeecc8oBBxxQ1lhjjVonxOuwww4bIs80MNL89re/raTsuOOOq23xDLZLPK5Dnoca3i86Ah2BjkBHYEohMCpRUVOC5dOf/nR5+ctfXg0ZaT0ee+yxsskmm5Ttt9++hl9zzTV1CWHJJZesgoRgQzzuv//+KlQYPs4333xlt912q0sGhKB82QdcdNFFdbZrNsx+IoJvSqE0B1cGnjQMF154YRXcjEzZptByvPCFLyz/8A//ULUO+uRlL3tZ2W677aqmAiH5zW9+U/tM/6+//vrlda973RAZCCT6EZm47rrr6jLKlVdeWTVjiIoyOJoQ/e9Qjn520ObE0eAovyUSwl7ykpfUpSfkCFl50YteVLUsnsc4y0fyzbPDONZx5JFHVm1RlraOPvrousxEg6PODz/8cMUm+fRzR6Aj0BHoCEw9BGZKVAz+1OinnnpqncWayRJ0zmbCu+yyS10muOCCC8o666xTttxyyzrj/tGPflTf0vCmhiUEBpze9qDyp/r/2te+Vo466qj61gYbiM9//vO1nAi3qQfVnFkjAhmZQEpoD/RnyIUwhIHwJ+idhcXRcqy11lrlxz/+cbUfQmqG6x/LL8jr2muvXbU0DFnZmYirzC222KJq1BAF5XDqgTSlPM8Sp06tQ5Y8Y694xSvqc7fccssVNigXX3xxeeSRR8ree+89RFDadEiQN4yUd/jhh9clxQUWWKD8/d//fSVnylE+W5hBctTm0687Ah2BjkBHYPYiMCpRyQyVsDGgMzzkGCOaxZ5xxhll5513rjNwdgJHHHFEVduLc/bZZ1f1Py0Kt8QSS1TVPZU80nPeeeeVnXbaqWy00UY1HPGh1s/SUPXsPxNGIALfmVYCYYxjY0KAIwfrrbfeDGGJI53+t6zCwNkyC62MZaLB5ZKrrrqqasoIfEavJ510UjWcfdOb3lTz8OxYZsrbQAgMEsR2JESFga84uU89LNfQjrBfYeOCNHPsVLTDc7TxxhvXdG3aL33pS7VOyy67bNUGekUZ4VlttdUq0QnhYruiPWlvyu3njkBHoCPQEZgaCIxKVFJFQs0slnNtWcAygBkt4WOQp06fd955k6QaQLJ/4AgCQsSbIt74kMY3NfI2hjhm/IRSd5OLALKBENBM6DdkAKl0TVjThnznO9+phSKJNBde35VOv9I6WHahDfnoRz9aBX2Ee2qKBHglmHZDGho33ytBVOLkLc+Qn1e/+tVV6+J5otVRv1NOOaWSp6Rx9kwgP54vGhFLhZylRVobb/0gzbRB2sRZiqTR+8AHPjD03Rb+6kiLd+KJJxbfZkG8TjvttFqnmrD/dAQ6Ah2BjsCUQ2BMRIVwiaYjs1YDPaNHQoawIaDambZXXL11wSWNV1DNrglIArG1URBPPt1NLgL67tprry2+U+ItGbYgtBn6RN/xz9svSInlG0SF06dxPgpH48VeBcnI84Do6FekU/7KQzqvvvrqst9++w2Rh+Sn733bxHLS8ccfX7NHMNjRICtIUOvED4F1nWfJs7bYYotVbc8///M/z6AVoQWi6RMnS0ry9HG49773vTWdD70x6LVkyS6nbWtbfr/uCHQEOgIdgdmLwJiICgHkmxTW/L2+imCY/XrLg2CiIQlZSXMsEVgyeN/73lc1Kd/73veqbYDZrDSMM88666xq12LGbmZMUEUQJZ9+nhgCMI7w9Ypv67zBo89oVzbbbLOy++67t8H1WvqWePI85phjqpZCuh133LHGY7fCiJptUpxXlNkr+ZibV5g5dUE0kBpv7yAzq6++evXX58iTN4c8Z76jg8jyp/3xfZ3YqESzl7app2cPOUk5DL05r0QnHk2MNrOX4SdvRMpyEM2P5czErYn7T0egI9AR6AhMCQTGRFQIAjNQs+QzzzyzGk5a+vHxLC4DfwZ6Z7Ynv/zlL6t63pKQV0XNutkbCGeT4p7gkLevjLKDiMCZEujM4ZUgxNM/zu5pVGhOkBBaEZqxxNEv4iQdTUf6lGbCWzcEu35HOoTddNNNlezIQ54OZILBtDfEEBV+niEH8uBtL3lZHhImH2VZ3rEUg9yGUCCv3iCy7MNZ5hEf0aBJUQZClG+oKEP9hefa0hYjWq9IKyf+6onU0DjlI3C1kP7TEegIdAQ6AlMGgbmeeuqpP+r3h6mWQZ9gQjoYKPpGBg1LuzxAmDByRDgsJ0TwyI4/gSK+V0ojJCIQfWyM0LOcYLZtWUD+3T07CCAn+pC9kOUX9iTIY1xICgLhI240GYxkI/z1rTSW/fj7JgpDVzYiXPpVvyNFngnkwDOCKNDmZCknZQ539pxwSI1lp7yZ41V3y4rK8UwK1xYktyVWiIp2IjFsXLQbMZGvME4bQ6KSfw3oPx2BjkBHoCMwZRAYlahk5urtCp85p5qPE2aQN+i3wk147gevxY+f9FyEBgFC0HAJqzf9Z9IR0D8wTj+1/dIW1oYnTfpLvPS/68E+G0yb+zZe/Nr0KSf1yL10Kdu1svO8JD3/xE/eObdxXMc/57ZeKbufOwIdgY5AR2D2IzCmpR8ajhg5tgN6Kzg0JYN+26w2fhue64TLK9dt+n49+QgE55zTF4MlDYaLFz9xB+/b9ImXvN3nOvESJ/fOg365HyxruOcl+Q+ek4f8E5Yy27D49XNHoCPQEegITB0EZqpRMZCzLzGDtSyTgX1wwJ86Teo16Qh0BDoCHYGOQEdguiAwJqISFXtIynRpfG9HR6Aj0BHoCHQEOgJTG4FRicrUrnqvXUegI9AR6Ah0BDoC0x2BP27sMt1b2tvXEegIdAQ6Ah2BjsAch0AnKnNcl/UKdwQ6Ah2BjkBH4LmDQCcqz52+7i3tCHQEOgIdgY7AHIdAJypzXJf1CncEOgIdgY5AR+C5g0AnKs+dvu4t7Qh0BDoCHYGOwByHQCcqc1yXTazCvoPj2zcOr5nnaP2Sc8JyP9Fz+zp7W04+j5981S1xU7Zz0ji3cZJu8CwO16ZLnOSf+/bchrXli5Mweea6TTsZ16nvWPNSD2m49OtY8Blr/sPFU2aLrzjBI/XXr/EbLo/u1xHoCHQEJoJAJyoTQW0OTONbOPbbsRsxweLLrg6bDT788MPDCvfJaGYEFyGm7AjWNu8IXXFTL2fO1g02ToyQbNPlWjrh2pi8EhYhKr82LGmkS5jwtvzcO9t5uU2f/Gf1rHy7UT/xxBNjzkrdpdF39jDSr882SdB2h3Ls4aTc4KGPfvKTn8ywpcGYG9MjdgQ6Ah2BmSAwpk/oJ48MTO4NlrPbpT5ToS6zG4vRyg9OBx98cNlwww3rQfC+9KUvLRdddFElA0cccUT53e9+V4WNDfoivEfLd6xhyr/xxhvLZz7zmfK1r31tSMC16cUheO1ybIdkwvCxxx6r5Ob9739/3Y27je+67Xfpr7rqqtqeueeee6iMxCHMt99++7L++uvXdOKHpPzgBz+omxcSwtr9spe9rAp/9UAg7L687LLLlmOOOaZuqJg8B+szkXvk6uSTT67lnXjiiTO0abj81FsdP/WpT5UPfvCDlbB8/etfrxuHCnu2nDYr9/7776+bS+rLlOcZsvP1HnvsMeSnHpOJ07PVrp5vR6AjMPURGDNRMSgRHnY4tiPuC1/4wtq6sQ5GbbwMcLMKz7/927/VnZftyjyRPAkq6SaSdlbr/udOT5jcfvvtdcdku2Db5fqAAw4oV155Zd0N+Z577ikveclLqsA5/vjjh/Z2mtV6Em5I0Re/+MVKRBCWt73tbTNoSIK/XbTt1myrBvUh/E444YRKnqS76aabar3N5u22/YEPfKAKT3VUjl2d55133vLWt751SEhGwF577bXlRz/60RBRkSYamK9+9avlrrvuqmQELsstt1xtP8KDtNh9ebzP+2i45b+gznYWv/nmm+tu1HarXmyxxWaoe7DJWb6ukTi7UNulOoSrLZMfp4y4PO/BhH/qkjhtOYkvLP633HJL3b1af9o5G6n1XNmZ2nOD7HFbbbVVWWKJJf4k/5TTzx2BjkBHYKwIjImoGKT+8R//sfzt3/5tFWaHH354nb2++c1vLqutttqYysqgRzjEDQ6S8R/LWZ1233338trXvrYcd9xx4x4QDajqZLDnZqUuY6nv7Ixz8cUXl+uuu64K4wsvvLAKaLPxj3/842WLLbYoK6+8ctUW0BgQzIQybFohN5H6R7ideuqpZZllliknnXRS+chHPlLz3mCDDWqWEWxuPBtvf/vbq786/PKXv6x+PK6//vryv//7v5VM/PrXv66aGZoWgjJ1RXQQlfnmm2+oPyOUkZ/22Uvd5O0ZoFF6wxveUMse7WdWnxN1lYe6uD722GMrYSToP/axj5Wjjz66LL744jVOytIPyMBZZ51VXvSiF9Vll1/84hclm4UicUia+PDcZZddypve9KbaDP9bGpdPfOITVYMmXH5XXHFFTfPhD394huYiTeedd179Ty244IJDYfCCP6Jy1FFHVZKCgCpTfq973esq9uLx03+p/1Am/aIj0BHoCEwAgZkSFQOPAZUQ22+//cqmm25aByzaDANXO+Cn/AxQbZhrg7MB0myQoOSXuEk7nrOZtcEyri2PX5v3YJh7Agr5WnHFFcvqq69es2nTJN858awd2qjvzLot+Wjr448/XgUUe5F77723aijYqBBgCMDCCy9c08xqm4M3YnT33XeXc889twpWJPeggw6qmoRdd911SGCLb4kFodIvNA3qo+6EOEKy9dZbV2Kq35EYfU8gIigcwXrDDTfU5RD3MAgOBPmaa65Z4xGscEkdpT/ttNPK8ssvP4N/jfzMj7IWWGCBss0229Q6z8pzkvoeeOCBFe+Qsy233LIgDoccckitq/+LctSVdmLnnXeubdV+eDr7DyKBsBFPP0Yro51w/NznPlc1NrDXZscDDzxQiesgUaF5Q2xMQkwEuLT1mmuuqX1Jo/qa17ym7LnnnrUOyvzhD3841A8IKe1ci3GLZb/uCHQEOgLjQWCmRIXdwOWXX16XfJ588sly33331Vmt9f5FF120DmKM6QxedllmC2BGaDC0xv/ggw/WgXOhhRaqthDW1gkX91TsZr8TdQbiCBtnA7cBkxCmJTD7bAdLqnUCLrO/733ve4VdAPJlsDWjNsBOF0fAwIjtgD7SP4QtgQyneeaZpyy11FJ1SUa79a0+4eAZbMeKRwSadATo2WefXb75zW9W2xRhhx12WCEsCU7LTrQENCyvf/3raxGWXfQZTYHnZv755y+W9RAXecbgFNnSrxH4NDaWdaQXj3B2HcdPvrQL7Fh22GGHssoqqyS4plFGq50ZCnzmzRo4Cm/b2MaZ2bX6ygP5ePTRR8snP/nJWn9alO9+97vF0hQbor/+67+uGgsaJMQE+ZIWWXPEIfyed/9J/71VV101QfUsDae+lmG0fe211x4i5PBwtA5uSKs6WRLcbbfdKjbwYyz7hS98oRIrY0JsiE455ZT6f0bkHNLKw3PVXUegI9ARmAwERiQqBjgDlAHJoGhAZNvAGNGMbaONNqqGi+wBqK8NTGwINtlkk7LXXnvVawZ3lhKo46mev//979dB2tr/+eefX2dkbBIy+I+1QeoVl2tEw+BPIBvACWX2DWwZxFGemSIhuNJKK5X999+/XH311TUbhEX7xFW38dYndZlK5+DiTGjB2UHIEygR5MJcEz4wjGNYy3ZDfHkQsiM52BGim222WY136aWXljPPPLPOypEVzwANHJyVgZxaXqDhMSu33LHOOutUcolgeub013/+53+WNdZYo87eEcpW+KpL+snyI0JjiYuw154LLrigEubYSUirHcKQZI4f4qDtlsDWW2+9kZo45K/+ll38J7jRcBEOR6RIHb2p89nPfrbQTNCAIEwcwf7QQw/Va0s2CAENJm2H/9AiiyxS80EClK0d3oRCNJDNb3zjGxVbeHh+LekheRzCaNlrp512qpoaBAjhGq5PabPkt+2229b/Oq0LEilf/+2NN964EkhtQmzVQZ2QPA6ObNhC9pXRXUegI9ARmFUERiQqBhkDFAFgMLrtttvK6aefXgdN6ngDfAYigs8MjPrcMgqHJNBUGCAN0OK7N7OTL0HGGfhnNtjXiDP5IXQM/kgUx+bATJtgoGFxzQDQwGtmrg5U7N5CITAsRcmD/3Ry2gNfs3aajHe84x21eTAgWKKdoOrPEkT6XhjsCKKRcOEvH2SEgyEjWMuEyGycPAmwPDPqtPfeexfLHcijdPyEe3YIbPnoN+RYnLjkkfs3vvGNdfmDoLVcRBCLjxDR2hDWnjPaNOQIUfHMeiY57aSdueOOO+pbR+2zLVzdPB/e/FG2vKThZvbsEuoOThlLLrlkfWPH/yRt5p/28aNVQVAQGORSmQiWCQCsOcsrCKK83/KWtxTLaOkjxMR1cEL8kA82Lt4wOvTQQ4fi1sye+UFiPB9wt5yEjPm/qNOOO+5YY0XLYjKgzy2nRbOlbjRWwrrrCHQEOgKThcCIRCWDnoIMPAYrg3tcBkH3lnvMfFtjRDNIxABx8JZHnLySNgN1wtpzO9C2/iNdG8iV9S//8i/F66Y0KmbxHCFguYPmx9JDlhqEKSftGm+ZI9VlKvpbhjPbJqThgXw88sgjQ9oFbWdLQuiZVYszaL8ws3ZF+K+11lo1qjz1cQhBm56/8Faj5rlAphAMRAUhJpzZl+hXBISzdCguwpA+Q7zyDIinfUsvvXQt31s7hLq3UmgapAkpkh9sLLEgOkg1Q19xEo92hz2P50aZljaR3PE4QpxAR9Y5/4PhSE7aJUyd1IGT1hJPnAkEDOVDm0Vz1Lqk4ycORyuFsDj0r/Rx8P3xj39ctYo0WbSOlqO0M/XU9vSbPvUf87ZUNCow1g/D9XfK6eeOQEegIzBeBEYkKiETyTADt/vBMPcGPs5A5t6sjVA48sgjyyWXXFKsZVNVG+wyAxNfvuInz3bwrBmO8YcgMMs02Jr5IikGWPkbSGl7hG+33XZ1Fs+QkUu5KSb1yf2cftYemJohs81hy0E7RrOFTBI0MBCHdonxa4TgeNuefk0fyjdCbhDn3Kd+4rFnufXWW8s555xTZ/OEnmVDB6FpKQIJIUhpEpSXfKQnKGlN9DeNhxk/0kWTgMSw1fCctgQHGSLIkQ/1tryB2LSOPU+Er7M6j9cpn0va5DdcPsFM3LTPMhxjY/VXX/ZEsKCl8tYPA3V5wkA/M/oddOxY3vOe91Rto3YifHHe9rGUg8zIhxaGXYq2e3ZSb/VxIKUIkqW79Lt6+Y/RYHFt/VNOP3cEOgIdgfEiMCJRaTPKINX6DV5HOOVs8LLsgxgYIM1W3/Wud1UhkZmxAVl86npr9GbR0hEkGaAHyxnpnpqaDQ3BK39LUdT8GSwJLOv+hJVZLZsEM1RlZUaoPmNp60h1mIr+2kPwIG8OpJHBLIGFCOTNDkLG0keWcGalLWPtu8RTR/2gfp/+9KcrqSDsIrDVxT3Nhrd61Nt9BKS07p2lQVJoi9h6ELzeeKL1Q565tp/Zd9A0ycuzKF40bMGA0J5dLs+v5SAaIuRCXyHk6qvNnu0Yr4ZAjFRfGkW2RHCkzYq77LLLyj777FOfh/wHvP7tf4WopK+U6f+FzDIKtjTnXniIYuxjUveU0c8dgY5AR2AiCIyJqBicqHmdOQOQe4MkR11ukOLEMWgRiIwACQ2zwBgwMiwkQMzoLRVZT6dp8cbBGWecUQ0rY2dSMxzlpy3XV1ZpVXznQX5eszSoE0pmmwTSCiusUO6888765oL4HOHorSaqc4ac0kyHAVY/aLu+MPtm4EojgaTQJrF/0G59SEARfGxK9FkE1SjQT2pQhCDbiLiWTPBj28FOhOYDKfF6LqFMU2HmT3NHu0AAayPBLi77Kks5wqTffPPNK3FB3himIrfIDMePbYhnpXXwYwczu5y+tJyWJTX1YJjuefe/81xrW+vyPxQHXpy+9n+znGPSYAmIozWxXMrgHU5xNEv+R7QmISP+WyYDjGtNMPRLnjP5WyLzdhfMaW266wh0BDoCs4rAmIiKwYvRXIS4gcm9mRyhhnAYsDgzU34GMepqaQkRJIAzoFoPJxC8qkytb/BjCCu+mZ1XVkOCIsRq4md+QiTkSStiUGZwSWPCSBcZkh+bC7NPAsirybQsZpGWocwIOYLMWyfeTjGb18bp4PQRBz994cxGx6ycQ95iT8DwFZn06jJsg2+NOEk/8qSZ0FeDLnVN2e6R0BisSpOlDHH0kVeuEx+xNbPPTF7+NCqWuZCWGIIiywSpt3s8E55VGgaCnlMuoUwDI+88e54pAn+yXcqQt/aO5NRDXM41PPz3tMG1pTAueIiTNP4fqXv8tF+f+29K4wuziEWejdQLmaFlozmJ9jF94tME++67b+3TWvgzdfOxOVop/+nuOgIdgY7AZCAw11NPPTXTBXcDHIc8ZBDLIGmgbFXwGSSTJpWUjhv0l2fysEYuXitwkn7wLI06cMhI6jAYT/6J14ZJz0VIuubX5tvGn5OvW8zTD2x4GKl626d1CW/9ZvVa+YSX13LzOfq2Tm3+KR/RoDFBQvglfsLdt9fJg58+1zY2FK2GIHGE6/fkyV+/I1KWAWkPWsceRr4IknQpt40zK9cIlWPQNmakPJWP2CADNJuICuIw+Jz7XwzaxshT+jz32g0HR3BJucEnZN+ZfROD67EQ+snGKfXq545AR+C5hcCYiApIDDo5Msi1UAnLgJf4GeicDYhxGeylyUDpOmQjftLxH87xd6SM4eK0fuIayJNfyhCHX1un4drX5jUnXqe9wTRtjP+fo00pe2b9pk4RuumvsdZPfAJ3UEArW76cs/YHg9bP9WCZecYGBXnNbBZ/2ra6TlmjZZs0LY6DdROWdshzMF/3bZzEbTHhJ568nYUl3Wj162EdgY5AR2AyEZjrD3/4Q2UCBqCZuQxmGcASX9oM/vFrz4nvbLBLPoNx2vCx1CcDe/Jv06SM1k95uW/DXbd5JKyt33S6btsXPP4c7QvGo5WlPuKNJe5I+Qy2L3kmfvJu296mSTznpE14m6aNN6vXqdNY8mnj5tp5uP/WSP/LpGvbN1zbxOMSNng/lvr2OB2BjkBHYLwItGPNXP/3f//3dAatzJjGm2GP3xHoCHQEOgIdgY5AR2AyEKDF5XAT2vUXhKRE1T4ZhfQ8OgIdgY5AR6Aj0BHoCEwEgZaP4CgvoNLFXnz6mtFgG2EiBfQ0HYGOQEegI9AR6Ah0BCaCAGLC+fSCvQS9UFGNaQV4C4RVf9aiJ1JAT9MR6Ah0BDoCHYGOQEdgVhDAQ9jX+baWtxuH3vrpBGVWYO1pOwIdgY5AR6Aj0BGYbAQoUv4fYUcVHFB/N+IAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据聚合\n",
    "聚合指的是任何能够从数组产生标量值的数据转换过程。之前的例子已经用过一些，比如mean、count、min以及sum等。\n",
    "![image.png](attachment:image.png)\n",
    "你可以使用自己发明的聚合运算，还可以调用分组对象上已经定义好的任何方法。例如，quantile可以计算Series或DataFrame列的样本分位数。<br>\n",
    "虽然quantile并没有明确地实现于GroupBy，但它是一个Series方法，所以这里是能用的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>0.5</th>\n",
       "      <th>pe</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>上海</th>\n",
       "      <td>31.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>云南</th>\n",
       "      <td>33.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>内蒙</th>\n",
       "      <td>26.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京</th>\n",
       "      <td>33.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>吉林</th>\n",
       "      <td>28.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0.5      pe\n",
       "area       \n",
       "上海    31.52\n",
       "云南    33.54\n",
       "内蒙    26.33\n",
       "北京    33.03\n",
       "吉林    28.52"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('area')[['pe']].quantile(0.5).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果要使用你自己的聚合函数，只需将其传入aggregate或agg方法即可："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>outstanding</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>上海</th>\n",
       "      <td>392.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>云南</th>\n",
       "      <td>64.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>内蒙</th>\n",
       "      <td>315.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京</th>\n",
       "      <td>2940.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>吉林</th>\n",
       "      <td>31.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      outstanding\n",
       "area             \n",
       "上海         392.51\n",
       "云南          64.46\n",
       "内蒙         315.07\n",
       "北京        2940.38\n",
       "吉林          31.50"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('area')[['outstanding']].agg(lambda x:x.max()-x.min()).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 面向列的多函数应用\n",
    "对不同的列使用不同的聚合函数，或一次应用多个函数。<br>\n",
    "通过自定义函数调用聚合函数的名字，运行聚合运算。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>pe</th>\n",
       "      <th>outstanding</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area</th>\n",
       "      <th>industry</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">上海</th>\n",
       "      <th>专用机械</th>\n",
       "      <td>182.131</td>\n",
       "      <td>1.278000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中成药</th>\n",
       "      <td>33.840</td>\n",
       "      <td>4.526667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>乳制品</th>\n",
       "      <td>11.365</td>\n",
       "      <td>8.135000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>互联网</th>\n",
       "      <td>46.272</td>\n",
       "      <td>11.534000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>仓储物流</th>\n",
       "      <td>191.845</td>\n",
       "      <td>2.541667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    pe  outstanding\n",
       "area industry                      \n",
       "上海   专用机械      182.131     1.278000\n",
       "     中成药        33.840     4.526667\n",
       "     乳制品        11.365     8.135000\n",
       "     互联网        46.272    11.534000\n",
       "     仓储物流      191.845     2.541667"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['area','industry'])[['pe','outstanding']].agg(\"mean\").head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果传入一组函数或函数名，得到的DataFrame的列就会以相应的函数命名："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">pe</th>\n",
       "      <th colspan=\"2\" halign=\"left\">outstanding</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area</th>\n",
       "      <th>industry</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">上海</th>\n",
       "      <th>专用机械</th>\n",
       "      <td>182.131</td>\n",
       "      <td>285.879127</td>\n",
       "      <td>1.278000</td>\n",
       "      <td>1.857877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中成药</th>\n",
       "      <td>33.840</td>\n",
       "      <td>22.354948</td>\n",
       "      <td>4.526667</td>\n",
       "      <td>2.804288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>乳制品</th>\n",
       "      <td>11.365</td>\n",
       "      <td>16.072537</td>\n",
       "      <td>8.135000</td>\n",
       "      <td>5.805347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>互联网</th>\n",
       "      <td>46.272</td>\n",
       "      <td>48.200422</td>\n",
       "      <td>11.534000</td>\n",
       "      <td>17.064092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>仓储物流</th>\n",
       "      <td>191.845</td>\n",
       "      <td>371.388803</td>\n",
       "      <td>2.541667</td>\n",
       "      <td>3.576777</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    pe             outstanding           \n",
       "                  mean         std        mean        std\n",
       "area industry                                            \n",
       "上海   专用机械      182.131  285.879127    1.278000   1.857877\n",
       "     中成药        33.840   22.354948    4.526667   2.804288\n",
       "     乳制品        11.365   16.072537    8.135000   5.805347\n",
       "     互联网        46.272   48.200422   11.534000  17.064092\n",
       "     仓储物流      191.845  371.388803    2.541667   3.576777"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['area','industry'])[['pe','outstanding']].agg([\"mean\",'std']).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">pe</th>\n",
       "      <th colspan=\"2\" halign=\"left\">outstanding</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>&lt;lambda&gt;</th>\n",
       "      <th>mean</th>\n",
       "      <th>&lt;lambda&gt;</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area</th>\n",
       "      <th>industry</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">上海</th>\n",
       "      <th>专用机械</th>\n",
       "      <td>182.131</td>\n",
       "      <td>777.98</td>\n",
       "      <td>1.278000</td>\n",
       "      <td>6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中成药</th>\n",
       "      <td>33.840</td>\n",
       "      <td>43.21</td>\n",
       "      <td>4.526667</td>\n",
       "      <td>5.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>乳制品</th>\n",
       "      <td>11.365</td>\n",
       "      <td>22.73</td>\n",
       "      <td>8.135000</td>\n",
       "      <td>8.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>互联网</th>\n",
       "      <td>46.272</td>\n",
       "      <td>122.22</td>\n",
       "      <td>11.534000</td>\n",
       "      <td>41.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>仓储物流</th>\n",
       "      <td>191.845</td>\n",
       "      <td>930.73</td>\n",
       "      <td>2.541667</td>\n",
       "      <td>9.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    pe          outstanding         \n",
       "                  mean <lambda>        mean <lambda>\n",
       "area industry                                       \n",
       "上海   专用机械      182.131   777.98    1.278000     6.12\n",
       "     中成药        33.840    43.21    4.526667     5.59\n",
       "     乳制品        11.365    22.73    8.135000     8.21\n",
       "     互联网        46.272   122.22   11.534000    41.00\n",
       "     仓储物流      191.845   930.73    2.541667     9.22"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['area','industry'])[['pe','outstanding']].agg([\"mean\",lambda x:x.max()-x.min()]).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "你并非一定要接受GroupBy自动给出的那些列名，特别是lambda函数，它们的名称是'<lambda>'，这样的辨识度就很低了。<br>\n",
    "因此，如果传入的是一个由(name,function)元组组成的列表，则各元组的第一个元素就会被用作DataFrame的列名（可以将这种二元元组列表看做一个有序映射）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">pe</th>\n",
       "      <th colspan=\"2\" halign=\"left\">outstanding</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>平均值</th>\n",
       "      <th>离散度</th>\n",
       "      <th>平均值</th>\n",
       "      <th>离散度</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area</th>\n",
       "      <th>industry</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">上海</th>\n",
       "      <th>专用机械</th>\n",
       "      <td>182.131</td>\n",
       "      <td>777.98</td>\n",
       "      <td>1.278000</td>\n",
       "      <td>6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中成药</th>\n",
       "      <td>33.840</td>\n",
       "      <td>43.21</td>\n",
       "      <td>4.526667</td>\n",
       "      <td>5.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>乳制品</th>\n",
       "      <td>11.365</td>\n",
       "      <td>22.73</td>\n",
       "      <td>8.135000</td>\n",
       "      <td>8.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>互联网</th>\n",
       "      <td>46.272</td>\n",
       "      <td>122.22</td>\n",
       "      <td>11.534000</td>\n",
       "      <td>41.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>仓储物流</th>\n",
       "      <td>191.845</td>\n",
       "      <td>930.73</td>\n",
       "      <td>2.541667</td>\n",
       "      <td>9.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    pe         outstanding       \n",
       "                   平均值     离散度         平均值    离散度\n",
       "area industry                                    \n",
       "上海   专用机械      182.131  777.98    1.278000   6.12\n",
       "     中成药        33.840   43.21    4.526667   5.59\n",
       "     乳制品        11.365   22.73    8.135000   8.21\n",
       "     互联网        46.272  122.22   11.534000  41.00\n",
       "     仓储物流      191.845  930.73    2.541667   9.22"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['area','industry'])[['pe','outstanding']].agg([('平均值',\"mean\"),('离散度',lambda x:x.max()-x.min())]).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如你所见，结果DataFrame拥有层次化的列，这相当于分别对各列进行聚合，然后用concat将结果组装到一起。<br>\n",
    "现在，假设你想要对一个列或不同的列应用不同的函数。具体的办法是向agg传入一个从列名映射到函数的字典："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">pe</th>\n",
       "      <th>outstanding</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>平均值</th>\n",
       "      <th>最大值</th>\n",
       "      <th>离散度</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area</th>\n",
       "      <th>industry</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">上海</th>\n",
       "      <th>专用机械</th>\n",
       "      <td>182.131</td>\n",
       "      <td>777.98</td>\n",
       "      <td>6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中成药</th>\n",
       "      <td>33.840</td>\n",
       "      <td>58.76</td>\n",
       "      <td>5.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>乳制品</th>\n",
       "      <td>11.365</td>\n",
       "      <td>22.73</td>\n",
       "      <td>8.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>互联网</th>\n",
       "      <td>46.272</td>\n",
       "      <td>122.22</td>\n",
       "      <td>41.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>仓储物流</th>\n",
       "      <td>191.845</td>\n",
       "      <td>949.04</td>\n",
       "      <td>9.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    pe         outstanding\n",
       "                   平均值     最大值         离散度\n",
       "area industry                             \n",
       "上海   专用机械      182.131  777.98        6.12\n",
       "     中成药        33.840   58.76        5.59\n",
       "     乳制品        11.365   22.73        8.21\n",
       "     互联网        46.272  122.22       41.00\n",
       "     仓储物流      191.845  949.04        9.22"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['area','industry'])[['pe','outstanding']]. \\\n",
    "agg({'pe':[('平均值','mean'),('最大值',max)],'outstanding':[('离散度',lambda x:x.max()-x.min())]}).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 以“没有行索引”的形式返回聚合数据\n",
    "到目前为止，所有示例中的聚合数据都有由唯一的分组键组成的索引（可能还是层次化的）。由于并不总是需要如此，所以你可以向groupby传入as_index=False以禁用该功能："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>industry</th>\n",
       "      <th colspan=\"2\" halign=\"left\">pe</th>\n",
       "      <th>outstanding</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>平均值</th>\n",
       "      <th>最大值</th>\n",
       "      <th>离散度</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>上海</td>\n",
       "      <td>专用机械</td>\n",
       "      <td>182.131</td>\n",
       "      <td>777.98</td>\n",
       "      <td>6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>上海</td>\n",
       "      <td>中成药</td>\n",
       "      <td>33.840</td>\n",
       "      <td>58.76</td>\n",
       "      <td>5.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>上海</td>\n",
       "      <td>乳制品</td>\n",
       "      <td>11.365</td>\n",
       "      <td>22.73</td>\n",
       "      <td>8.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>上海</td>\n",
       "      <td>互联网</td>\n",
       "      <td>46.272</td>\n",
       "      <td>122.22</td>\n",
       "      <td>41.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>上海</td>\n",
       "      <td>仓储物流</td>\n",
       "      <td>191.845</td>\n",
       "      <td>949.04</td>\n",
       "      <td>9.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  area industry       pe         outstanding\n",
       "                     平均值     最大值         离散度\n",
       "0   上海     专用机械  182.131  777.98        6.12\n",
       "1   上海      中成药   33.840   58.76        5.59\n",
       "2   上海      乳制品   11.365   22.73        8.21\n",
       "3   上海      互联网   46.272  122.22       41.00\n",
       "4   上海     仓储物流  191.845  949.04        9.22"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['area','industry'],as_index=False)[['pe','outstanding']]. \\\n",
    "agg({'pe':[('平均值','mean'),('最大值',max)],'outstanding':[('离散度',lambda x:x.max()-x.min())]}).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当然，对结果调用reset_index也能得到这种形式的结果。使用as_index=False方法可以避免一些不必要的计算。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>industry</th>\n",
       "      <th colspan=\"2\" halign=\"left\">pe</th>\n",
       "      <th>outstanding</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>平均值</th>\n",
       "      <th>最大值</th>\n",
       "      <th>离散度</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>上海</td>\n",
       "      <td>专用机械</td>\n",
       "      <td>182.131</td>\n",
       "      <td>777.98</td>\n",
       "      <td>6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>上海</td>\n",
       "      <td>中成药</td>\n",
       "      <td>33.840</td>\n",
       "      <td>58.76</td>\n",
       "      <td>5.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>上海</td>\n",
       "      <td>乳制品</td>\n",
       "      <td>11.365</td>\n",
       "      <td>22.73</td>\n",
       "      <td>8.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>上海</td>\n",
       "      <td>互联网</td>\n",
       "      <td>46.272</td>\n",
       "      <td>122.22</td>\n",
       "      <td>41.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>上海</td>\n",
       "      <td>仓储物流</td>\n",
       "      <td>191.845</td>\n",
       "      <td>949.04</td>\n",
       "      <td>9.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  area industry       pe         outstanding\n",
       "                     平均值     最大值         离散度\n",
       "0   上海     专用机械  182.131  777.98        6.12\n",
       "1   上海      中成药   33.840   58.76        5.59\n",
       "2   上海      乳制品   11.365   22.73        8.21\n",
       "3   上海      互联网   46.272  122.22       41.00\n",
       "4   上海     仓储物流  191.845  949.04        9.22"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['area','industry'])[['pe','outstanding']]. \\\n",
    "agg({'pe':[('平均值','mean'),('最大值',max)],'outstanding':[('离散度',lambda x:x.max()-x.min())]}).reset_index().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# apply：一般性的“拆分－应用－合并”\n",
    "最通用的GroupBy方法是apply,apply会将待处理的对象拆分成多个片段，然后对各片段调用传入的函数，最后尝试将各片段组合到一起。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>industry</th>\n",
       "      <th>area</th>\n",
       "      <th>pe</th>\n",
       "      <th>outstanding</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area</th>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">上海</th>\n",
       "      <th>600732</th>\n",
       "      <td>区域地产</td>\n",
       "      <td>上海</td>\n",
       "      <td>1454.96</td>\n",
       "      <td>4.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603918</th>\n",
       "      <td>软件服务</td>\n",
       "      <td>上海</td>\n",
       "      <td>2235.35</td>\n",
       "      <td>1.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600610</th>\n",
       "      <td>建筑施工</td>\n",
       "      <td>上海</td>\n",
       "      <td>3016.25</td>\n",
       "      <td>3.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">云南</th>\n",
       "      <th>300142</th>\n",
       "      <td>生物制药</td>\n",
       "      <td>云南</td>\n",
       "      <td>340.06</td>\n",
       "      <td>13.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002428</th>\n",
       "      <td>小金属</td>\n",
       "      <td>云南</td>\n",
       "      <td>716.34</td>\n",
       "      <td>6.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601099</th>\n",
       "      <td>证券</td>\n",
       "      <td>云南</td>\n",
       "      <td>759.17</td>\n",
       "      <td>64.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">内蒙</th>\n",
       "      <th>300355</th>\n",
       "      <td>环境保护</td>\n",
       "      <td>内蒙</td>\n",
       "      <td>122.18</td>\n",
       "      <td>11.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600262</th>\n",
       "      <td>汽车整车</td>\n",
       "      <td>内蒙</td>\n",
       "      <td>138.95</td>\n",
       "      <td>1.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600863</th>\n",
       "      <td>火力发电</td>\n",
       "      <td>内蒙</td>\n",
       "      <td>694.07</td>\n",
       "      <td>58.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">北京</th>\n",
       "      <th>600764</th>\n",
       "      <td>通信设备</td>\n",
       "      <td>北京</td>\n",
       "      <td>1069.30</td>\n",
       "      <td>3.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300352</th>\n",
       "      <td>软件服务</td>\n",
       "      <td>北京</td>\n",
       "      <td>1209.67</td>\n",
       "      <td>10.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            industry area       pe  outstanding\n",
       "area code                                      \n",
       "上海   600732     区域地产   上海  1454.96         4.46\n",
       "     603918     软件服务   上海  2235.35         1.77\n",
       "     600610     建筑施工   上海  3016.25         3.76\n",
       "云南   300142     生物制药   云南   340.06        13.50\n",
       "     002428      小金属   云南   716.34         6.44\n",
       "     601099       证券   云南   759.17        64.79\n",
       "内蒙   300355     环境保护   内蒙   122.18        11.58\n",
       "     600262     汽车整车   内蒙   138.95         1.70\n",
       "     600863     火力发电   内蒙   694.07        58.08\n",
       "北京   600764     通信设备   北京  1069.30         3.30\n",
       "     300352     软件服务   北京  1209.67        10.44"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('area').apply(lambda x:x.sort_values(by='pe')[-3:]).head(11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "函数在DataFrame的各个片段上调用，然后结果由pandas.concat组装到一起，并以分组名称进行了标记。于是，最终结果就有了一个层次化索引，其内层索引值来自原DataFrame。<br>\n",
    "如果传给apply的函数能够接受其他参数或关键字，则可以将这些内容放在函数名后面一并传入："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(x,field='pe',n=3):\n",
    "    return x.sort_values(by=field)[-n:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>industry</th>\n",
       "      <th>area</th>\n",
       "      <th>pe</th>\n",
       "      <th>outstanding</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area</th>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">上海</th>\n",
       "      <th>600000</th>\n",
       "      <td>银行</td>\n",
       "      <td>上海</td>\n",
       "      <td>5.06</td>\n",
       "      <td>281.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601328</th>\n",
       "      <td>银行</td>\n",
       "      <td>上海</td>\n",
       "      <td>5.21</td>\n",
       "      <td>392.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">云南</th>\n",
       "      <th>600497</th>\n",
       "      <td>铅锌</td>\n",
       "      <td>云南</td>\n",
       "      <td>15.71</td>\n",
       "      <td>43.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601099</th>\n",
       "      <td>证券</td>\n",
       "      <td>云南</td>\n",
       "      <td>759.17</td>\n",
       "      <td>64.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">内蒙</th>\n",
       "      <th>601216</th>\n",
       "      <td>化工原料</td>\n",
       "      <td>内蒙</td>\n",
       "      <td>8.81</td>\n",
       "      <td>84.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600010</th>\n",
       "      <td>普钢</td>\n",
       "      <td>内蒙</td>\n",
       "      <td>27.73</td>\n",
       "      <td>316.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">北京</th>\n",
       "      <th>601398</th>\n",
       "      <td>银行</td>\n",
       "      <td>北京</td>\n",
       "      <td>6.18</td>\n",
       "      <td>2696.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601288</th>\n",
       "      <td>银行</td>\n",
       "      <td>北京</td>\n",
       "      <td>5.27</td>\n",
       "      <td>2940.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">吉林</th>\n",
       "      <th>601929</th>\n",
       "      <td>影视音像</td>\n",
       "      <td>吉林</td>\n",
       "      <td>46.14</td>\n",
       "      <td>31.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600881</th>\n",
       "      <td>水泥</td>\n",
       "      <td>吉林</td>\n",
       "      <td>0.00</td>\n",
       "      <td>32.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>四川</th>\n",
       "      <th>000629</th>\n",
       "      <td>小金属</td>\n",
       "      <td>四川</td>\n",
       "      <td>0.00</td>\n",
       "      <td>47.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            industry area      pe  outstanding\n",
       "area code                                     \n",
       "上海   600000       银行   上海    5.06       281.04\n",
       "     601328       银行   上海    5.21       392.51\n",
       "云南   600497       铅锌   云南   15.71        43.10\n",
       "     601099       证券   云南  759.17        64.79\n",
       "内蒙   601216     化工原料   内蒙    8.81        84.38\n",
       "     600010       普钢   内蒙   27.73       316.77\n",
       "北京   601398       银行   北京    6.18      2696.12\n",
       "     601288       银行   北京    5.27      2940.55\n",
       "吉林   601929     影视音像   吉林   46.14        31.11\n",
       "     600881       水泥   吉林    0.00        32.23\n",
       "四川   000629      小金属   四川    0.00        47.67"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('area').apply(f,field='outstanding',n=2).head(11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pe</th>\n",
       "      <th>outstanding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3536.000000</td>\n",
       "      <td>3536.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>85.620803</td>\n",
       "      <td>13.470546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>297.688646</td>\n",
       "      <td>85.736708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>14.685000</td>\n",
       "      <td>1.790000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>31.820000</td>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>67.190000</td>\n",
       "      <td>9.565000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10609.500000</td>\n",
       "      <td>2940.550000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 pe  outstanding\n",
       "count   3536.000000  3536.000000\n",
       "mean      85.620803    13.470546\n",
       "std      297.688646    85.736708\n",
       "min        0.000000     0.000000\n",
       "25%       14.685000     1.790000\n",
       "50%       31.820000     4.500000\n",
       "75%       67.190000     9.565000\n",
       "max    10609.500000  2940.550000"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th colspan=\"8\" halign=\"left\">pe</th>\n",
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       "    <tr>\n",
       "      <th>area</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>上海</th>\n",
       "      <td>283.0</td>\n",
       "      <td>14.369293</td>\n",
       "      <td>37.735218</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.575</td>\n",
       "      <td>4.61</td>\n",
       "      <td>9.62</td>\n",
       "      <td>392.51</td>\n",
       "      <td>283.0</td>\n",
       "      <td>91.653746</td>\n",
       "      <td>276.708323</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.4600</td>\n",
       "      <td>31.52</td>\n",
       "      <td>64.160</td>\n",
       "      <td>3016.25</td>\n",
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       "    <tr>\n",
       "      <th>云南</th>\n",
       "      <td>33.0</td>\n",
       "      <td>11.038182</td>\n",
       "      <td>13.198717</td>\n",
       "      <td>0.33</td>\n",
       "      <td>3.030</td>\n",
       "      <td>6.76</td>\n",
       "      <td>13.50</td>\n",
       "      <td>64.79</td>\n",
       "      <td>33.0</td>\n",
       "      <td>103.613030</td>\n",
       "      <td>181.767848</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.9200</td>\n",
       "      <td>33.54</td>\n",
       "      <td>100.510</td>\n",
       "      <td>759.17</td>\n",
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       "    <tr>\n",
       "      <th>内蒙</th>\n",
       "      <td>25.0</td>\n",
       "      <td>30.212000</td>\n",
       "      <td>63.247214</td>\n",
       "      <td>1.70</td>\n",
       "      <td>4.850</td>\n",
       "      <td>11.37</td>\n",
       "      <td>26.74</td>\n",
       "      <td>316.77</td>\n",
       "      <td>25.0</td>\n",
       "      <td>63.537600</td>\n",
       "      <td>137.184649</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.2300</td>\n",
       "      <td>26.33</td>\n",
       "      <td>73.420</td>\n",
       "      <td>694.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京</th>\n",
       "      <td>310.0</td>\n",
       "      <td>57.010032</td>\n",
       "      <td>279.222247</td>\n",
       "      <td>0.17</td>\n",
       "      <td>2.540</td>\n",
       "      <td>5.77</td>\n",
       "      <td>14.81</td>\n",
       "      <td>2940.55</td>\n",
       "      <td>310.0</td>\n",
       "      <td>95.557581</td>\n",
       "      <td>181.475243</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.5225</td>\n",
       "      <td>33.03</td>\n",
       "      <td>93.005</td>\n",
       "      <td>1318.61</td>\n",
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       "    <tr>\n",
       "      <th>吉林</th>\n",
       "      <td>41.0</td>\n",
       "      <td>9.376829</td>\n",
       "      <td>8.305006</td>\n",
       "      <td>0.73</td>\n",
       "      <td>3.360</td>\n",
       "      <td>6.70</td>\n",
       "      <td>12.13</td>\n",
       "      <td>32.23</td>\n",
       "      <td>41.0</td>\n",
       "      <td>100.873415</td>\n",
       "      <td>245.862436</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.2600</td>\n",
       "      <td>28.52</td>\n",
       "      <td>57.890</td>\n",
       "      <td>1383.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     outstanding                                                             \\\n",
       "           count       mean         std   min    25%    50%    75%      max   \n",
       "area                                                                          \n",
       "上海         283.0  14.369293   37.735218  0.00  1.575   4.61   9.62   392.51   \n",
       "云南          33.0  11.038182   13.198717  0.33  3.030   6.76  13.50    64.79   \n",
       "内蒙          25.0  30.212000   63.247214  1.70  4.850  11.37  26.74   316.77   \n",
       "北京         310.0  57.010032  279.222247  0.17  2.540   5.77  14.81  2940.55   \n",
       "吉林          41.0   9.376829    8.305006  0.73  3.360   6.70  12.13    32.23   \n",
       "\n",
       "         pe                                                                 \n",
       "      count        mean         std  min      25%    50%      75%      max  \n",
       "area                                                                        \n",
       "上海    283.0   91.653746  276.708323  0.0  14.4600  31.52   64.160  3016.25  \n",
       "云南     33.0  103.613030  181.767848  0.0   9.9200  33.54  100.510   759.17  \n",
       "内蒙     25.0   63.537600  137.184649  0.0   9.2300  26.33   73.420   694.07  \n",
       "北京    310.0   95.557581  181.475243  0.0  12.5225  33.03   93.005  1318.61  \n",
       "吉林     41.0  100.873415  245.862436  0.0  14.2600  28.52   57.890  1383.56  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('area').describe().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th rowspan=\"8\" valign=\"top\">上海</th>\n",
       "      <th>count</th>\n",
       "      <td>283.000000</td>\n",
       "      <td>283.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>91.653746</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>276.708323</td>\n",
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       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
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       "      <th>25%</th>\n",
       "      <td>14.460000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>31.520000</td>\n",
       "      <td>4.610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>64.160000</td>\n",
       "      <td>9.620000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3016.250000</td>\n",
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       "      <th rowspan=\"3\" valign=\"top\">云南</th>\n",
       "      <th>count</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>33.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>103.613030</td>\n",
       "      <td>11.038182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>181.767848</td>\n",
       "      <td>13.198717</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                     pe  outstanding\n",
       "area                                \n",
       "上海   count   283.000000   283.000000\n",
       "     mean     91.653746    14.369293\n",
       "     std     276.708323    37.735218\n",
       "     min       0.000000     0.000000\n",
       "     25%      14.460000     1.575000\n",
       "     50%      31.520000     4.610000\n",
       "     75%      64.160000     9.620000\n",
       "     max    3016.250000   392.510000\n",
       "云南   count    33.000000    33.000000\n",
       "     mean    103.613030    11.038182\n",
       "     std     181.767848    13.198717"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('area').apply(lambda x:x.describe()).head(11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>上海</th>\n",
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       "      <td>276.708323</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.4600</td>\n",
       "      <td>31.52</td>\n",
       "      <td>64.160</td>\n",
       "      <td>3016.25</td>\n",
       "      <td>283.0</td>\n",
       "      <td>14.369293</td>\n",
       "      <td>37.735218</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.575</td>\n",
       "      <td>4.61</td>\n",
       "      <td>9.62</td>\n",
       "      <td>392.51</td>\n",
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       "    <tr>\n",
       "      <th>云南</th>\n",
       "      <td>33.0</td>\n",
       "      <td>103.613030</td>\n",
       "      <td>181.767848</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.9200</td>\n",
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       "      <td>100.510</td>\n",
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       "      <td>3.030</td>\n",
       "      <td>6.76</td>\n",
       "      <td>13.50</td>\n",
       "      <td>64.79</td>\n",
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       "    <tr>\n",
       "      <th>内蒙</th>\n",
       "      <td>25.0</td>\n",
       "      <td>63.537600</td>\n",
       "      <td>137.184649</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.2300</td>\n",
       "      <td>26.33</td>\n",
       "      <td>73.420</td>\n",
       "      <td>694.07</td>\n",
       "      <td>25.0</td>\n",
       "      <td>30.212000</td>\n",
       "      <td>63.247214</td>\n",
       "      <td>1.70</td>\n",
       "      <td>4.850</td>\n",
       "      <td>11.37</td>\n",
       "      <td>26.74</td>\n",
       "      <td>316.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京</th>\n",
       "      <td>310.0</td>\n",
       "      <td>95.557581</td>\n",
       "      <td>181.475243</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.5225</td>\n",
       "      <td>33.03</td>\n",
       "      <td>93.005</td>\n",
       "      <td>1318.61</td>\n",
       "      <td>310.0</td>\n",
       "      <td>57.010032</td>\n",
       "      <td>279.222247</td>\n",
       "      <td>0.17</td>\n",
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       "      <td>5.77</td>\n",
       "      <td>14.81</td>\n",
       "      <td>2940.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>吉林</th>\n",
       "      <td>41.0</td>\n",
       "      <td>100.873415</td>\n",
       "      <td>245.862436</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.2600</td>\n",
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       "      <td>1383.56</td>\n",
       "      <td>41.0</td>\n",
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       "      <td>8.305006</td>\n",
       "      <td>0.73</td>\n",
       "      <td>3.360</td>\n",
       "      <td>6.70</td>\n",
       "      <td>12.13</td>\n",
       "      <td>32.23</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "         pe                                                                 \\\n",
       "      count        mean         std  min      25%    50%      75%      max   \n",
       "area                                                                         \n",
       "上海    283.0   91.653746  276.708323  0.0  14.4600  31.52   64.160  3016.25   \n",
       "云南     33.0  103.613030  181.767848  0.0   9.9200  33.54  100.510   759.17   \n",
       "内蒙     25.0   63.537600  137.184649  0.0   9.2300  26.33   73.420   694.07   \n",
       "北京    310.0   95.557581  181.475243  0.0  12.5225  33.03   93.005  1318.61   \n",
       "吉林     41.0  100.873415  245.862436  0.0  14.2600  28.52   57.890  1383.56   \n",
       "\n",
       "     outstanding                                                             \n",
       "           count       mean         std   min    25%    50%    75%      max  \n",
       "area                                                                         \n",
       "上海         283.0  14.369293   37.735218  0.00  1.575   4.61   9.62   392.51  \n",
       "云南          33.0  11.038182   13.198717  0.33  3.030   6.76  13.50    64.79  \n",
       "内蒙          25.0  30.212000   63.247214  1.70  4.850  11.37  26.74   316.77  \n",
       "北京         310.0  57.010032  279.222247  0.17  2.540   5.77  14.81  2940.55  \n",
       "吉林          41.0   9.376829    8.305006  0.73  3.360   6.70  12.13    32.23  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('area').apply(lambda x:x.describe()).unstack().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 禁止分组键\n",
    "从上面的例子中可以看出，分组键会跟原始对象的索引共同构成结果对象中的层次化索引。将group_keys=False传入groupby即可禁止该效果："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pe</th>\n",
       "      <th>outstanding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>283.000000</td>\n",
       "      <td>283.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>91.653746</td>\n",
       "      <td>14.369293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>276.708323</td>\n",
       "      <td>37.735218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>14.460000</td>\n",
       "      <td>1.575000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               pe  outstanding\n",
       "count  283.000000   283.000000\n",
       "mean    91.653746    14.369293\n",
       "std    276.708323    37.735218\n",
       "min      0.000000     0.000000\n",
       "25%     14.460000     1.575000"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['area'],group_keys=False).apply(lambda x:x.describe()).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 透视表和交叉表\n",
    "透视表（pivot table）是各种电子表格程序和其他数据分析软件中一种常见的数据汇总工具。它根据一个或多个键对数据进行聚合，并根据行和列上的分组键将数据分配到各个矩形区域中。<br>\n",
    "**注意：**计算分组平均数（pivot_table的默认聚合类型）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"21\" halign=\"left\">pe</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>industry</th>\n",
       "      <th>专用机械</th>\n",
       "      <th>中成药</th>\n",
       "      <th>乳制品</th>\n",
       "      <th>互联网</th>\n",
       "      <th>仓储物流</th>\n",
       "      <th>供气供热</th>\n",
       "      <th>保险</th>\n",
       "      <th>元器件</th>\n",
       "      <th>全国地产</th>\n",
       "      <th>公共交通</th>\n",
       "      <th>...</th>\n",
       "      <th>钢加工</th>\n",
       "      <th>铁路</th>\n",
       "      <th>铅锌</th>\n",
       "      <th>铜</th>\n",
       "      <th>铝</th>\n",
       "      <th>银行</th>\n",
       "      <th>陶瓷</th>\n",
       "      <th>食品</th>\n",
       "      <th>饲料</th>\n",
       "      <th>黄金</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>上海</th>\n",
       "      <td>182.13100</td>\n",
       "      <td>33.840</td>\n",
       "      <td>11.365</td>\n",
       "      <td>46.272000</td>\n",
       "      <td>191.845000</td>\n",
       "      <td>99.760000</td>\n",
       "      <td>20.630</td>\n",
       "      <td>32.243333</td>\n",
       "      <td>10.004000</td>\n",
       "      <td>37.302</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.610000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.305</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>云南</th>\n",
       "      <td>NaN</td>\n",
       "      <td>51.140</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.85</td>\n",
       "      <td>27.54</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.430</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>内蒙</th>\n",
       "      <td>NaN</td>\n",
       "      <td>76.060</td>\n",
       "      <td>19.930</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.380</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京</th>\n",
       "      <td>67.73125</td>\n",
       "      <td>36.520</td>\n",
       "      <td>58.180</td>\n",
       "      <td>89.756364</td>\n",
       "      <td>25.703333</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>13.605</td>\n",
       "      <td>104.880000</td>\n",
       "      <td>42.138333</td>\n",
       "      <td>51.260</td>\n",
       "      <td>...</td>\n",
       "      <td>159.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.25</td>\n",
       "      <td>5.437778</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.475</td>\n",
       "      <td>99.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>吉林</th>\n",
       "      <td>NaN</td>\n",
       "      <td>28.508</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 110 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 pe                                                            \\\n",
       "industry       专用机械     中成药     乳制品        互联网        仓储物流       供气供热      保险   \n",
       "area                                                                            \n",
       "上海        182.13100  33.840  11.365  46.272000  191.845000  99.760000  20.630   \n",
       "云南              NaN  51.140     NaN        NaN         NaN        NaN     NaN   \n",
       "内蒙              NaN  76.060  19.930        NaN         NaN        NaN  17.380   \n",
       "北京         67.73125  36.520  58.180  89.756364   25.703333   8.400000  13.605   \n",
       "吉林              NaN  28.508     NaN        NaN         NaN  10.056667     NaN   \n",
       "\n",
       "                                         ...                              \\\n",
       "industry         元器件       全国地产    公共交通  ...       钢加工  铁路     铅锌      铜   \n",
       "area                                     ...                               \n",
       "上海         32.243333  10.004000  37.302  ...      0.00 NaN    NaN  53.09   \n",
       "云南               NaN   0.000000     NaN  ...       NaN NaN  51.85  27.54   \n",
       "内蒙               NaN        NaN     NaN  ...       NaN NaN  33.87    NaN   \n",
       "北京        104.880000  42.138333  51.260  ...    159.08 NaN  25.60    NaN   \n",
       "吉林               NaN        NaN     NaN  ...       NaN NaN    NaN    NaN   \n",
       "\n",
       "                                                      \n",
       "industry      铝        银行  陶瓷      食品      饲料     黄金  \n",
       "area                                                  \n",
       "上海          NaN  5.610000 NaN  17.305     NaN    NaN  \n",
       "云南         0.00       NaN NaN  31.430     NaN    NaN  \n",
       "内蒙          NaN       NaN NaN   0.000     NaN  56.50  \n",
       "北京        42.25  5.437778 NaN     NaN  20.475  99.46  \n",
       "吉林        17.24       NaN NaN     NaN     NaN    NaN  \n",
       "\n",
       "[5 rows x 110 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(values=['pe'],index=['area'],columns=['industry']).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
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       "      <th>上海</th>\n",
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       "      <td>46.272000</td>\n",
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       "      <td>20.630</td>\n",
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       "      <th>云南</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>51.140</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>51.85</td>\n",
       "      <td>27.54</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.430</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>103.613030</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>56.50</td>\n",
       "      <td>63.537600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京</th>\n",
       "      <td>67.73125</td>\n",
       "      <td>36.520</td>\n",
       "      <td>58.180</td>\n",
       "      <td>89.756364</td>\n",
       "      <td>25.703333</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>13.605</td>\n",
       "      <td>104.880000</td>\n",
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       "      <td>51.260</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>42.25</td>\n",
       "      <td>5.437778</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>20.475</td>\n",
       "      <td>99.46</td>\n",
       "      <td>95.557581</td>\n",
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       "      <th>吉林</th>\n",
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       "      <td>0.000000</td>\n",
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       "</table>\n",
       "<p>5 rows × 111 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 pe                                                            \\\n",
       "industry       专用机械     中成药     乳制品        互联网        仓储物流       供气供热      保险   \n",
       "area                                                                            \n",
       "上海        182.13100  33.840  11.365  46.272000  191.845000  99.760000  20.630   \n",
       "云南          0.00000  51.140   0.000   0.000000    0.000000   0.000000   0.000   \n",
       "内蒙          0.00000  76.060  19.930   0.000000    0.000000   0.000000  17.380   \n",
       "北京         67.73125  36.520  58.180  89.756364   25.703333   8.400000  13.605   \n",
       "吉林          0.00000  28.508   0.000   0.000000    0.000000  10.056667   0.000   \n",
       "\n",
       "                                            ...                                \\\n",
       "industry         元器件       全国地产    公共交通     ...       铁路     铅锌      铜      铝   \n",
       "area                                        ...                                 \n",
       "上海         32.243333  10.004000  37.302     ...      0.0   0.00  53.09   0.00   \n",
       "云南          0.000000   0.000000   0.000     ...      0.0  51.85  27.54   0.00   \n",
       "内蒙          0.000000   0.000000   0.000     ...      0.0  33.87   0.00   0.00   \n",
       "北京        104.880000  42.138333  51.260     ...      0.0  25.60   0.00  42.25   \n",
       "吉林          0.000000   0.000000   0.000     ...      0.0   0.00   0.00  17.24   \n",
       "\n",
       "                                                            \n",
       "industry        银行   陶瓷      食品      饲料     黄金         All  \n",
       "area                                                        \n",
       "上海        5.610000  0.0  17.305   0.000   0.00   91.653746  \n",
       "云南        0.000000  0.0  31.430   0.000   0.00  103.613030  \n",
       "内蒙        0.000000  0.0   0.000   0.000  56.50   63.537600  \n",
       "北京        5.437778  0.0   0.000  20.475  99.46   95.557581  \n",
       "吉林        0.000000  0.0   0.000   0.000   0.00  100.873415  \n",
       "\n",
       "[5 rows x 111 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(values=['pe'],index=['area'],columns=['industry'],aggfunc=\"mean\",margins=True,fill_value=0).head()"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 交叉表：crosstab\n",
    "交叉表（cross-tabulation，简称crosstab）是一种用于计算分组频率的特殊透视表。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>industry</th>\n",
       "      <th>专用机械</th>\n",
       "      <th>中成药</th>\n",
       "      <th>乳制品</th>\n",
       "      <th>互联网</th>\n",
       "      <th>仓储物流</th>\n",
       "      <th>供气供热</th>\n",
       "      <th>保险</th>\n",
       "      <th>元器件</th>\n",
       "      <th>全国地产</th>\n",
       "      <th>公共交通</th>\n",
       "      <th>...</th>\n",
       "      <th>铁路</th>\n",
       "      <th>铅锌</th>\n",
       "      <th>铜</th>\n",
       "      <th>铝</th>\n",
       "      <th>银行</th>\n",
       "      <th>陶瓷</th>\n",
       "      <th>食品</th>\n",
       "      <th>饲料</th>\n",
       "      <th>黄金</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>上海</th>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>云南</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>内蒙</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京</th>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>吉林</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>四川</th>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>天津</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>宁夏</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>安徽</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>山东</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>山西</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广东</th>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广西</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新疆</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>江苏</th>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>江西</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>河北</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>河南</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>浙江</th>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>海南</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>深圳</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>湖北</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>湖南</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>甘肃</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>福建</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西藏</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贵州</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>辽宁</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>重庆</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>陕西</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>青海</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>黑龙江</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>128</td>\n",
       "      <td>75</td>\n",
       "      <td>11</td>\n",
       "      <td>52</td>\n",
       "      <td>35</td>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "      <td>162</td>\n",
       "      <td>37</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>58</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>3536</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33 rows × 111 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "industry  专用机械  中成药  乳制品  互联网  仓储物流  供气供热  保险  元器件  全国地产  公共交通  ...   铁路  铅锌  \\\n",
       "area                                                            ...            \n",
       "上海          10    3    2    5     6     1   1    3     5     5  ...    0   0   \n",
       "云南           0    3    0    0     0     0   0    0     1     0  ...    0   2   \n",
       "内蒙           0    1    1    0     0     0   1    0     0     0  ...    0   2   \n",
       "北京           8    2    1   11     3     1   2    6     6     1  ...    0   2   \n",
       "吉林           0    5    0    0     0     3   0    0     0     0  ...    0   0   \n",
       "四川           7    2    0    1     2     1   0    3     0     0  ...    0   1   \n",
       "天津           1    3    0    0     1     1   0    1     0     0  ...    0   0   \n",
       "宁夏           0    0    0    0     0     0   0    0     0     0  ...    0   0   \n",
       "安徽           5    0    0    1     1     1   0    3     0     0  ...    0   0   \n",
       "山东           4    3    0    3     1     2   0    4     0     0  ...    0   0   \n",
       "山西           1    2    0    0     0     1   0    0     0     0  ...    1   0   \n",
       "广东          11    8    1    2     5     2   0   32     3     0  ...    0   0   \n",
       "广西           0    3    1    0     0     0   0    0     0     0  ...    0   0   \n",
       "新疆           0    0    2    0     1     5   0    0     0     0  ...    0   0   \n",
       "江苏          25    2    0    7     6     1   0   15     1     1  ...    0   0   \n",
       "江西           2    2    0    0     0     0   0    3     0     1  ...    0   0   \n",
       "河北           2    1    0    0     0     0   0    2     1     0  ...    0   0   \n",
       "河南           3    3    1    1     0     0   0    4     0     0  ...    0   1   \n",
       "浙江          24    5    1    6     2     1   0   17     5     0  ...    0   0   \n",
       "海南           0    0    0    0     0     0   0    0     1     0  ...    0   0   \n",
       "深圳           7    1    0    3     4     1   1   42     8     0  ...    1   1   \n",
       "湖北           4    3    0    2     0     1   1    8     1     1  ...    0   0   \n",
       "湖南           0    5    0    2     0     0   0    5     0     0  ...    0   1   \n",
       "甘肃           1    3    1    0     0     0   0    0     0     0  ...    0   0   \n",
       "福建           4    1    0    4     1     0   0    9     2     0  ...    0   0   \n",
       "西藏           0    3    0    0     0     0   0    0     0     0  ...    0   2   \n",
       "贵州           0    4    0    0     0     1   0    2     0     0  ...    0   0   \n",
       "辽宁           8    0    0    2     1     3   0    0     1     0  ...    1   1   \n",
       "重庆           0    1    0    1     0     1   0    1     2     0  ...    0   1   \n",
       "陕西           1    3    0    0     0     1   0    2     0     0  ...    0   0   \n",
       "青海           0    1    0    1     0     0   0    0     0     0  ...    0   0   \n",
       "黑龙江          0    2    0    0     1     0   0    0     0     0  ...    0   0   \n",
       "All        128   75   11   52    35    28   6  162    37     9  ...    3  14   \n",
       "\n",
       "industry   铜   铝  银行  陶瓷  食品  饲料  黄金   All  \n",
       "area                                        \n",
       "上海         1   0   3   0   4   0   0   283  \n",
       "云南         1   1   0   0   1   0   0    33  \n",
       "内蒙         0   0   0   0   1   0   1    25  \n",
       "北京         0   1   9   0   0   2   1   310  \n",
       "吉林         0   1   0   0   0   0   0    41  \n",
       "四川         0   0   1   0   1   2   0   119  \n",
       "天津         0   0   0   0   1   0   0    50  \n",
       "宁夏         0   0   0   0   0   0   0    13  \n",
       "安徽         4   0   0   0   1   0   0   105  \n",
       "山东         0   2   0   1   8   1   4   194  \n",
       "山西         0   0   0   0   0   0   0    38  \n",
       "广东         1   3   0   4   9   2   0   304  \n",
       "广西         0   0   0   0   2   0   0    37  \n",
       "新疆         0   1   0   1   2   1   1    53  \n",
       "江苏         1   7   7   0   1   0   0   395  \n",
       "江西         1   0   0   0   1   1   0    39  \n",
       "河北         1   1   0   0   1   0   0    56  \n",
       "河南         0   3   0   0   4   0   0    78  \n",
       "浙江         2   2   2   1   4   2   0   426  \n",
       "海南         0   0   0   0   1   0   0    31  \n",
       "深圳         0   1   2   0   0   2   0   277  \n",
       "湖北         0   0   0   0   1   0   0    99  \n",
       "湖南         0   0   0   0   6   2   1   104  \n",
       "甘肃         1   0   0   0   0   0   2    33  \n",
       "福建         0   1   1   0   4   2   1   131  \n",
       "西藏         0   0   0   0   2   0   0    17  \n",
       "贵州         0   0   1   0   0   0   0    29  \n",
       "辽宁         0   0   0   0   2   1   0    72  \n",
       "重庆         0   0   0   0   1   0   0    49  \n",
       "陕西         0   0   0   0   0   0   0    47  \n",
       "青海         1   0   0   0   0   0   0    12  \n",
       "黑龙江        0   1   0   0   0   0   0    36  \n",
       "All       14  25  26   7  58  18  11  3536  \n",
       "\n",
       "[33 rows x 111 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=df.area,columns=df.industry,margins=True)"
   ]
  }
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